CN114002517B

CN114002517B - Device diagnosis method, platform, system and readable storage medium

Info

Publication number: CN114002517B
Application number: CN202010736161.2A
Authority: CN
Inventors: 陈世静; 胡宗权
Original assignee: BYD Co Ltd
Current assignee: BYD Co Ltd
Priority date: 2020-07-28
Filing date: 2020-07-28
Publication date: 2024-07-16
Anticipated expiration: 2040-07-28
Also published as: CN114002517A

Abstract

The invention discloses a device diagnosis method, a device, a platform and a readable storage medium, which are used for solving the problem that safety risk exists in equipment caused by untimely device diagnosis of the equipment. The method comprises the following steps: acquiring health state evaluation parameters of the same type of device of the same type of equipment in real time, wherein the health state evaluation parameters are related to the current state of the device; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, wherein the suspicious parameters are health state evaluation parameters with preset errors with other health state evaluation parameters; if the suspicious parameters exist, diagnosing that the device corresponding to the suspicious parameters is in an unhealthy state.

Description

Device diagnosis method, platform, system and readable storage medium

Technical Field

The present invention relates to the field of device diagnosis technologies of apparatuses, and in particular, to a device diagnosis method, device, platform, and readable storage medium.

Background

In an electronic device or system (hereinafter referred to as a device), the health status of the device is extremely important, and the effectiveness, stability and reliability of the functions of each device in the device are precisely related to the performance of the device. In order to ensure the safety and reliability of the equipment, the performance of the device of the equipment needs to be monitored, and if the performance is unqualified, the device needs to be replaced in time to ensure the reliable operation of the equipment, wherein the service life index of the device is an important parameter index for measuring the performance of the device. For example, the device may refer to a contactor of a high voltage loop of a power battery on an electric vehicle. As can be seen, the power-on and power-off of the high-voltage circuit of the power battery are usually performed by closing and opening through contactors, and the contactors of the high-voltage circuit generally comprise contactors such as a positive contactor and a negative contactor, so that the performance of the contactors is closely related to the performance of the whole vehicle.

In the prior art, the method for diagnosing the service life of the device mainly comprises the following steps: at present, one or more parameters are generally obtained during periodic maintenance and inspection of equipment, so that the health state of the equipment, such as an automobile contactor, is diagnosed, the parameters of the contactor are required to be obtained during periodic maintenance and inspection of a vehicle, the service life of the contactor cannot be monitored in real time, the probability of inaccurate data of single detection is high, and the equipment diagnosis method in the prior art is not real-time enough, so that a certain safety risk exists in the equipment.

Disclosure of Invention

The invention provides a device diagnosis method, a platform, a system and a readable storage medium, which are used for solving the problem that the device diagnosis method in the prior art is not real-time enough, so that a certain safety risk exists in equipment.

In view of this, a first aspect of the present invention provides a device diagnostic method comprising:

Acquiring health state evaluation parameters of the same type of device of the same type of equipment in real time, wherein the health state evaluation parameters are related to the current state of the device;

determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, wherein the suspicious parameters are health state evaluation parameters with preset errors with other health state evaluation parameters;

If the suspicious parameters exist, diagnosing that the device corresponding to the suspicious parameters is in an unhealthy state.

Further, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device comprises:

Determining a first mean value and a first standard deviation corresponding to all health state evaluation parameters;

And determining whether the health state evaluation parameters of all devices in the same type of device are suspicious parameters according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters.

Further, determining whether the health state evaluation parameters of the devices in the same type of device are suspicious parameters according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters comprises:

Determining the absolute value of the difference between the health state evaluation parameters of all devices in the same type of devices and the first mean value to obtain first absolute errors corresponding to all devices;

determining the ratio of the first absolute error to the first standard deviation corresponding to each device;

determining a comparison coefficient according to the quantity of all health state evaluation parameters, wherein the comparison coefficient and the quantity are in positive correlation;

If the ratio of the first absolute error corresponding to the target device to the first standard deviation in each device is greater than or equal to the comparison coefficient, determining the health state evaluation parameter corresponding to the target device as the suspicious parameter.

Determining the difference value between the health state evaluation parameters of all devices in the same type of devices and the first mean value to obtain a target error value corresponding to each device;

Determining the ratio of the target error value corresponding to each device to the first standard deviation;

If the ratio of the target error value corresponding to the target device to the first standard deviation is greater than or equal to the Grabbs critical value in each device, determining that the health state evaluation parameter corresponding to the target device is a suspicious parameter, wherein the Grabbs critical value is related to the preset significance level coefficient and the quantity of all the health state evaluation parameters.

Determining a second mean value and a second standard deviation corresponding to the remaining health state evaluation parameters in all the health state evaluation parameters, wherein the remaining health state evaluation parameters are all health state evaluation parameters except the health state evaluation parameters corresponding to the target device;

Determining the absolute value of the difference value between the health state evaluation parameter corresponding to the target device and the second average value to obtain a second absolute error corresponding to the target device;

Determining the ratio of the second absolute error corresponding to the target device to the second standard deviation;

If the ratio of the second absolute error corresponding to the target device to the second standard deviation is greater than or equal to the roman nofski test coefficient, determining that the health state evaluation parameter corresponding to the target device is a suspicious parameter, wherein the roman nofski test coefficient is related to the preset significance level coefficient and the number of all health state evaluation parameters.

a. Determining an extreme magnitude value according to the magnitude of all the health state evaluation parameters, wherein the extreme magnitude value comprises a maximum evaluation parameter and a minimum evaluation parameter;

b. Determining a statistic value corresponding to the extreme size value according to a Dixon statistic formula corresponding to the extreme size value;

c. Determining whether the statistical value is greater than or equal to a dirk threshold corresponding to the statistical value, wherein the dirk threshold is related to a preset significance level coefficient and the number of all health state evaluation parameters;

d. if the statistical value is greater than or equal to the Dixon critical value corresponding to the statistical value, determining the health state evaluation parameter corresponding to the extreme size value as a suspicious parameter, and eliminating the extreme size value from all the health state evaluation parameters;

e. and (3) repeatedly executing the steps a-e on the residual health state evaluation parameters with the extreme values removed until all suspicious parameters are determined.

Further, if there is no suspicious parameter, the method further includes:

Comparing each health state evaluation parameter of all health state evaluation parameters with a corresponding limit parameter;

if the health state evaluation parameter is larger than the corresponding limit parameter, determining that the device corresponding to the health state evaluation parameter is in an unhealthy state.

determining the number of all health state evaluation parameters of the same type of device;

determining a corresponding suspicious parameter diagnosis mode according to the quantity and the size;

diagnosing whether suspicious parameters exist in all the health state evaluation parameters according to the corresponding suspicious parameter diagnosis mode.

more than two suspicious parameter diagnosis modes are selected to determine whether suspicious parameters exist in all health state evaluation parameters of the same device;

if more than two suspicious parameter diagnosis modes determine that the target health state evaluation parameters are suspicious parameters, determining that the target health state evaluation parameters are suspicious parameters;

If the diagnosis results of more than two suspicious parameter diagnosis modes are contradictory, the health state evaluation parameters of all devices of the same device are acquired again, and diagnosis of the suspicious parameters is carried out again.

Further, the health state evaluation parameter is the temperature of the device, the resistance value of both ends, or the voltage difference of both ends.

Further, all health state evaluation parameters of the same device are health state evaluation parameters acquired at the same time.

A second aspect of the present invention provides a device diagnostic platform comprising:

the acquisition module is used for acquiring health state evaluation parameters of the same device of the same type of equipment in real time, wherein the health state evaluation parameters are related to the current state of the device;

The determining module is used for determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, wherein the suspicious parameters are parameters with preset errors with other parameters of all health state evaluation parameters;

and the diagnosis module is used for diagnosing that the device corresponding to the suspicious parameter is in an unhealthy state if the suspicious parameter exists.

A third aspect of the present invention provides a device diagnostic system comprising a plurality of devices of the same type and a device diagnostic platform;

the device diagnosis platform is used for diagnosing the health state of the same type of device according to the health state evaluation parameters of the same type of device;

a device diagnostic platform for implementing a device diagnostic method as in any one of the preceding first aspects or for implementing a function as in the preceding second aspect.

A fourth aspect of the present invention provides a readable storage medium storing a computer program, characterized in that the computer program when executed by a processor performs the steps of the device diagnostic method as in any one of the preceding first aspects or performs the functions of the device diagnostic platform as in the preceding second aspect.

From the above technical scheme, the invention has the following advantages:

It can be seen that the invention provides a device diagnosis method, firstly, acquiring health state evaluation parameters of the same type of device of the same type of equipment in real time, wherein the health state evaluation parameters are related to the current state of the device; determining the health state evaluation parameters of the same type of devices according to the health state evaluation parameters of each device; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, wherein the suspicious parameters are health state evaluation parameters with preset errors with other health state evaluation parameters; if the suspicious parameters exist, determining that the device corresponding to the suspicious parameters is in an unhealthy state. Therefore, the embodiment of the invention can acquire the health state evaluation parameters of the same device of different devices in the same type in real time to determine the health state of each device, so that the service life of the device can be diagnosed, rather than judging the health state of each device when the vehicle is regularly maintained and checked, the service life of each device can be monitored in real time, the problem of inaccurate data caused by single detection during regular maintenance can be effectively reduced, and the safety of the device is improved.

Drawings

FIG. 1 is a schematic diagram of a system architecture of a device diagnostic system according to an embodiment of the present invention;

Fig. 2 is a schematic diagram of a whole electric vehicle high-voltage distribution principle in an embodiment of the invention;

FIG. 3 is a schematic diagram of another system architecture of a device diagnostic system according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of an embodiment of the present invention for collecting the current and terminal voltage of the main contact of the contactor;

FIG. 5 is a schematic flow chart of a device diagnosis method according to an embodiment of the present invention;

FIG. 6 is a schematic view of a contactor matrix numbering scheme according to an embodiment of the invention;

FIG. 7 is a schematic diagram of a device diagnosis platform according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of another embodiment of a device diagnostic platform.

Detailed Description

The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.

The invention provides a device diagnosis system and a device diagnosis method based on the device diagnosis system, and firstly, the device diagnosis system provided by the invention is described.

As shown in fig. 1, the present invention provides a device diagnosis system, where the device diagnosis system includes a device diagnosis platform and a plurality of devices of the same type, where the device diagnosis platform is connected to each device, for example, the plurality of devices of the same type include a device 1, a device 2, a device m, and each device of the plurality of devices of the same type may each feed back a health status evaluation parameter of a device of the same type to the device diagnosis platform, so that the device diagnosis platform obtains a health status of a device of each device according to the health status evaluation parameter of the device of the same type. It should be noted that, the same type of equipment refers to equipment of the same batch type or the same model, in an application scenario, the device diagnosis platform may be a large data platform, and the multiple same type of equipment refers to vehicles of the same batch type or the same model, and each vehicle may feed back relevant information of the same type of device of itself to the large data platform, so that the large data platform obtains the health states of the devices of each vehicle according to the health state evaluation parameters of the same type of device. The big data platform has stronger data processing capability, and the health state of the devices of each equipment is diagnosed through the big data platform, so that the processing efficiency and the instantaneity can be improved.

It should be noted that the device diagnosis system provided by the present invention may be applied to various industries or applications, and the device refers broadly to various electronic devices or systems (collectively referred to herein as devices), and the device refers broadly to various devices on the device. The device diagnosis system provided by the invention can be used for diagnosing the relevant contactor on the automobile, and the device diagnosis system provided by the invention can be used for diagnosing other relevant devices on other devices besides the relevant devices on the automobile, for example, can also be applied to the diagnosis of the relevant devices on high-voltage electrical equipment and the diagnosis of the relevant devices on a battery energy storage system, and particularly can be used for diagnosing the relevant device contactor on the high-voltage electrical equipment, a battery energy storage system and the like. The above-mentioned device is a contactor, and the device may be a device such as a pre-charge resistor and a fuse on a device, for example, the device may be a device such as a pre-charge resistor and a fuse on a high voltage module of an automobile, or a device such as a pre-charge resistor and a fuse on another device, which is not specifically limited and not described one by one.

The device diagnosis platform is used for acquiring the health state evaluation parameters of the devices uploaded by the devices in real time, wherein the health state evaluation parameters are associated with the current health state of the devices, and the device diagnosis platform can determine the health state evaluation parameters of the same type of devices according to the health state evaluation parameters of the devices; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, wherein the suspicious parameters are parameters with preset errors with other parameters of all health state evaluation parameters; if the suspicious parameters exist, the suspicious parameters are compared to be abrupt or abnormal relative to other parameters, so that the device corresponding to the suspicious parameters is determined to be in an unhealthy state, otherwise, the device is determined to be in a healthy state, and the follow-up user can confirm the suspicious parameters conveniently. The health state evaluation parameter may refer to a temperature of the device, a resistance value of two ends, or a voltage difference of two ends, and of course, the health state evaluation parameter may refer to other parameters that may reflect a monitoring state of the device.

For the sake of understanding, the device diagnosis system of the present invention will be described with respect to an application scenario in which the device is an electric vehicle and the device is a contactor on the electric vehicle. It will be appreciated that in order to achieve certain functions on an electric vehicle, the electric vehicle is typically deployed with a plurality of different types of contactors for related control of the power cell high voltage loop system or other circuitry. Taking a power battery high-voltage loop system on an electric automobile as an example, as shown in fig. 2, fig. 2 is a schematic diagram of a whole electric automobile high-voltage distribution, and the high-voltage loop system comprises a battery pack, a hall sensor, a safety switch, a maintenance switch, a power battery, an insulation monitoring module (insulation monitoring module, IMD) connected with two ends of the power battery, a diode, a thermosensitive heater (positive temperature coefficient, PTC), an electric device and various contactors. The electric device comprises a direct current charging port, an upper part, a driving module, an on-vehicle charger, a steering, DC-DC conversion module and an air conditioner compressor; each contactor includes a voltage division contactor 1, a PTC contactor 2, a direct current charging port positive electrode contactor 3, a direct current charging port negative electrode contactor 4, a top-up contactor 5, a main pre-charge contactor 6, a discharge main contactor 7, an auxiliary pre-charge contactor 8, an alternating current slow charge contactor 9, and an auxiliary contactor 10. The battery pack is used for providing power for the whole vehicle; the IMD is connected with two poles of the battery pack and used for monitoring the insulation state of the battery pack; the voltage division contactor 1 is connected with the negative electrode of the battery pack and controls the power on and power off of the high-voltage loop; one end of the Hall sensor is connected with the high-voltage main loop and is used for monitoring the total current of the high-voltage loop; the maintenance switch plays a role in high-voltage interlocking; the safety is connected with the main loop and each branch circuit and is used for protecting the overcurrent of the circuit; and each contactor is connected between the main loop and the electric appliance and is responsible for controlling the power on and power off of each branch electric appliance. The connection relationship between the components of the high-voltage circuit of the power battery of this example is specifically shown in fig. 2, and the specific connection relationship will not be described here. It should be noted that the circuit shown in fig. 2 is only illustrative in the embodiment of the invention, and is not limited to the embodiment of the invention.

Therefore, it is extremely important to know the health state of each contactor in real time, and in order to monitor the health state of the contactor in real time, the invention provides a device diagnosis platform which can diagnose the health state of the contactor on an automobile in real time.

In combination with the application scenario, taking the health state evaluation parameter as the resistance value of the main contact of the contactor as an example, how each electric automobile obtains the resistance value of the main contact of each contactor is described. As shown in fig. 3, the automobile may include a vehicle end acquisition module and a vehicle end calculation module, where the vehicle end acquisition module is connected with the vehicle end calculation module, and the vehicle end calculation module is connected with the device diagnosis platform and the whole automobile instrument respectively. The vehicle end acquisition module is used for acquiring the end voltage and the end current of the main contact points of the contactors, the vehicle end calculation module is used for calculating according to the end voltage and the end current of the main contact points of the contactors acquired by the vehicle end acquisition module so as to determine the resistance value of the main contact points of the contactors, and the vehicle end acquisition module is deployed on a vehicle and used for acquiring the current of a loop where the contactors are located and the end voltages of the two ends of the main contact points of the contactors. In the automobile, the vehicle end calculation module may refer to a BMS module on the automobile, and the BMS module is used to calculate the resistance of the main contact of the contactor.

The vehicle end acquisition module comprises a current sampling module and a voltage sampling module, wherein the current sampling module is used for acquiring the current of a loop where a contactor is located, the voltage sampling module is used for acquiring the end voltages of two ends of a main contact point of the contactor, as shown in fig. 4, fig. 4 is an acquisition schematic diagram for acquiring the current and the end voltage of the main contact point of the contactor to be tested, and the vehicle end acquisition module comprises a current sampling module 11 and a voltage sampling module 12, a contactor to be tested 13, a maintenance switch, a safety device and an electric device. Therefore, the current of the contactor 13 to be tested can be collected in real time by the current sampling module 11, and the terminal voltage of the contactor 13 to be tested can be collected in real time by the voltage sampling module 11. It should be noted that, the current sampling module 12 may be a current probe, which may acquire the current of the contactor to be tested in real time, and the voltage sampling module may be a voltage probe, which may implement acquiring the terminal voltages at two ends of the contactor to be tested. It should be noted that various types of contactors on the vehicle may be deployed in the manner shown in fig. 3, so that the terminal current and the terminal voltage of the various types of contactors on the vehicle may be obtained. It should be noted that, for other devices of the automobile or devices on other devices, the current and the terminal voltage of the device may be obtained in a manner as shown in fig. 4, which is not specifically described herein.

After the current sampling module 11 is used for collecting the current of the tested contactor 13 in real time and the voltage sampling module 12 is used for collecting the terminal voltage of the tested contactor 13 in real time, the vehicle terminal calculating module is used for obtaining the current of the tested contactor collected in real time from the current sampling module 11 and the terminal voltage of the tested contactor collected in real time from the voltage sampling module 12, the vehicle terminal calculating module 103 can calculate according to the current and the terminal voltage of the tested contactor after obtaining the current and the terminal voltage of the tested contactor collected in real time, so that the main contact resistance of the contactor is used as a health state evaluation parameter of the contactor, and the current sampling module 11 is used for collecting the current I of the tested contactor 13; the voltage sampling module 12 collects the voltage U of the main contact terminal of the contactor 13 to be tested; then, the resistance of the main contact end of the tested contactor is r=u/I, and the calculated resistance value of the main contact of the contactor can be uploaded to the device diagnosis platform.

Therefore, the device diagnosis platform can acquire the main contact resistance values of the contactors uploaded by the vehicle end calculation modules of the plurality of vehicles in real time, and for the device diagnosis platform, after acquiring the resistance value data of the main contacts of the contactors uploaded by the vehicle end calculation modules 103 of the plurality of vehicles in real time, the device diagnosis platform can diagnose the health state of the contactors according to the resistance value data of the main contacts of the contactors and feed the diagnosis result back to the vehicle end calculation modules, so that the vehicle end calculation modules display the diagnosis result through the vehicle instrument or the driver terminal equipment, and a driver can know the health state of the device in real time.

The above description is only given by taking the contactor and the resistance value of the main contact of the contactor as the health state evaluation parameter as examples, and the invention can also use the parameters of the voltage difference between the positive and negative poles, the temperature and the like of the device as the health state evaluation parameter to perform the health diagnosis of the device, and the invention is not limited in particular. After the device diagnosis platform acquires the health state evaluation parameters of the same type of device in real time, healthy and unhealthy devices can be determined according to the acquired health state evaluation parameters of the same type of device, and a device diagnosis method is described in detail below.

As shown in fig. 5, based on the above device diagnosis system, a device health diagnosis method is correspondingly provided, and in the following embodiments, for convenience of description and understanding, an apparatus will be taken as an automobile, and a device will be taken as a contactor for illustration, where the device diagnosis method includes the following steps:

S10: health state evaluation parameters of the same type of device of the same type of equipment are obtained in real time, and the health state evaluation parameters are related to the current health state of the device.

The health state evaluation parameter of the device is a parameter related to the health state of the device, and may refer to the temperature of the device, the resistance value of two ends, or the voltage difference of two ends, for example. The same type of device refers to the same type of device under the same type of equipment.

Taking a device as a contactor of a vehicle and a health state evaluation parameter as a resistance value of a main contact of the contactor as an example, the same type of device refers to the same type of contactor of the same type, and the same type of device can refer to vehicles of the same type or the same batch type, such as a voltage division contactor, a PTC contactor, a direct current charging port positive electrode contactor, a direct current charging port negative electrode contactor, an upper-mounted contactor, a main pre-charging contactor, a discharging main contactor, an auxiliary pre-charging contactor, an alternating current slow charging contactor or an auxiliary contactor on an automobile.

Referring to fig. 6, fig. 6 is a schematic view of a contactor matrix number, and it is assumed that the same type of vehicles include vehicles 1,2, … and m vehicles, and each contactor of the vehicles is classified according to the contactor type in each different vehicle (vehicle 1-vehicle m), and the contactor types of the vehicles are sequentially classified into a contactor type 1, a contactor type 2, … and a contactor type n. Since the vehicles belong to the same type, the types of contactors contained in each vehicle are generally the same, and the contactor types of each vehicle can be further divided according to the serial numbers of the vehicles, for example, each different type of contactor of the vehicle 1 can be sequentially divided into 1-1, 1-2, … and 1-n; each of the different types of contactors of the vehicle 2 may be sequentially divided into 2-1, 2-2, …, 2-n; each of the different types of contactors of the vehicle 3 may be sequentially divided into 3-1, 3-2, …, 3-n; similarly, the various types of contactors of the vehicle m may be sequentially divided into m-1, m-2, …, and m-n, so that a contactor matrix numbering schematic as shown in FIG. 6 may be obtained. The contact matrix numbers shown in fig. 6 are only examples, and are not limiting to the present invention, and other numbers may be used in other embodiments, so long as the device diagnosis platform can distinguish between each device type and the vehicle to which the device diagnosis platform belongs. It should be noted that, in other cases of the apparatus and the device, the numbering manner shown in fig. 6 may also be adopted, and the specific invention is not described.

In the vehicles 1-m, each vehicle can acquire the health state evaluation parameters of the own contactor in real time, and an exemplary specific acquisition mode can refer to a mode shown in fig. 4, namely, the automobile can acquire the current and terminal voltage of the contactor through a deployed vehicle terminal acquisition module, calculate the resistance value of the main contact of the contactor through a vehicle terminal calculation module according to the current and terminal voltage acquired by the vehicle terminal acquisition module, and finally upload the resistance value of the main contact of each contactor to a device diagnosis platform through the vehicle terminal calculation module. For the device diagnosis platform, the device diagnosis platform can acquire the resistance value of the main contact of the same contactor in the vehicle 1-vehicle m in real time, for example, the resistance value of the main contact of the contactor in the contactor type 1 is acquired, namely, the resistance values of the main contacts of the corresponding contactors 1-n, 2-n, … and m-n in the vehicle 1-vehicle m are acquired.

In addition, it should be noted that, in an embodiment, all health status evaluation parameters of the same device are health status evaluation parameters collected at the same time, and the invention is not limited.

S20: determining whether suspicious parameters exist in all health state evaluation parameters of the same device, and if the suspicious parameters exist, executing step S30; if there is no suspicious parameter, step S40 is performed.

After acquiring health state evaluation parameters of the same type of device in real time, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, wherein the suspicious parameters are health state evaluation parameters with preset errors with other health state evaluation parameters. It should be noted that the preset error is a preset error, and the preset error may be an error set according to an empirical value, or may be a coarse error determined according to a coarse error theory, or may be other error relationships, which is not limited in the embodiment of the present invention. It will be understood that, for the same type of device, the health status evaluation parameters of the same device of each device should be expected under the condition that the same device is normal, if the health status evaluation parameter of one device is out of expectation, it is indicated that the current health status evaluation parameter of the device is suspicious, and if the suspicious parameter exists, step S30 is performed; if there is no suspicious parameter, step S40 is performed.

For example, as shown in fig. 6, assuming that the resistances of the main contacts of the contactors 1-n, 2-n, …, and m-n are R _1n、R_2n、…、R_mn-1、R_mn, respectively, after R _1n、R_2n、…、R_mn-1、R_mn is obtained, it can be determined whether a suspicious parameter exists in R _1n、R_2n、…、R_mn-1、R_mn.

S30: and determining that the device corresponding to the suspicious parameter is in an unhealthy state.

S40: and determining that each device corresponding to all the health state evaluation parameters is in a health state.

For the steps S30-S40, after determining whether there are suspicious parameters in all the health status evaluation parameters of the same device, if there are suspicious parameters, determining that the device corresponding to the suspicious parameters is in an unhealthy status, which can also be understood as that the device corresponding to the suspicious parameters is in a damaged status; if the suspicious parameters do not exist, determining that all devices corresponding to the health state evaluation parameters are in the health state, and understanding that all devices corresponding to the health state evaluation parameters are in the undamaged state.

For example, after the main contact resistance R _1n、R_2n、…、R_mn-1、R_mn of the same type of contactor is obtained, it may be determined whether a suspicious parameter exists in R _1n、R_2n、…、R_mn-1、R_mn; for example, if it is determined that R _2n is a suspicious parameter, it is determined that R _2n is in an unhealthy state for the corresponding contactor, that is, the contactor n in the vehicle 2 is in an unhealthy state, and the same applies to other contactors, for example, the contactor 1-contactor n-1, which is not specifically described herein; if the suspicious parameters are not determined to exist in the R _1n、R_2n、…、R_mn-1、R_mn, the contactors corresponding to the R _1n、R_2n、…、R_mn-1、R_mn are determined to be in the health state. I.e. the contactors n in the vehicles 1-m are all in a healthy state.

In this embodiment, only the contactor and the health state evaluation parameter are described as examples, and the embodiment of the present invention is not limited thereto. The contactor may be other devices on the vehicle, or a contactor of other equipment, or other devices of other equipment; the health status evaluation parameters may be parameters such as temperature, voltage difference, etc. of the device, which are not specifically limited and not illustrated.

It can be seen that the embodiment of the invention provides a device diagnosis method, which includes the steps of acquiring health state evaluation parameters of the same type of device in real time, wherein the health state evaluation parameters are related to the current state of the device; determining the health state evaluation parameters of the same type of devices according to the health state evaluation parameters of each device; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, wherein the suspicious parameters are parameters with preset errors with other parameters of all health state evaluation parameters; if the suspicious parameters exist, determining that the device corresponding to the suspicious parameters is in an unhealthy state. Therefore, the embodiment of the invention can acquire the health state evaluation parameters of the same device of different devices in the same type in real time to determine the health state of each device, so that the service life of the device can be diagnosed, rather than judging the health state of each device when the vehicle is regularly maintained and checked, the service life of each device can be monitored in real time, the problem of inaccurate data caused by single detection during regular maintenance can be effectively reduced, and the safety of the device is improved.

It should be noted that, in the embodiment of the present invention, after the health status evaluation parameters of the same device of the same type of equipment are obtained in real time, there may be multiple ways of determining whether suspicious parameters exist in all the health status evaluation parameters of the same device, for example, one of the foregoing is to determine by using an error theory. The following description is made respectively:

In one embodiment, in step S20, that is, determining whether suspicious parameters exist in all health status assessment parameters of the same device includes the following steps:

S21: and determining a first mean value and a first standard deviation corresponding to all the health state evaluation parameters.

S22: and determining whether the health state evaluation parameters of all devices in the same type of device are suspicious parameters according to the first mean value and the first standard deviation corresponding to all the health state evaluation parameters.

For steps S21-S22, after the health status evaluation parameters of the same type of device are obtained, the mean value and standard deviation corresponding to all the health status evaluation parameters of the same type of device may be determined, so as to facilitate distinguishing other mean values and standard deviations appearing in the subsequent embodiments, in this embodiment, the mean value and standard deviation corresponding to all the health status evaluation parameters of the same type of device are respectively referred to as a first mean value and a first standard deviation, and according to the first mean value and the first standard deviation corresponding to all the health status evaluation parameters, it is determined whether the health status evaluation parameters of each device in the same type of device are suspicious parameters.

It will be appreciated that the first standard deviation of the set of all health assessment parameters reflects the accuracy of the individual health assessment parameters in the set, while the first mean is the number of quantities representing the trends in the set of all health assessment parameters, i.e. the first standard deviation and the first mean are the two most important reference values for the trend and degree of dispersion in the set of all health assessment parameters. When it is required to determine whether a health state evaluation parameter is normal, the first standard deviation and the first mean have larger reference meanings, so in the embodiment of the invention, whether the health state evaluation parameter corresponding to each device is normal can be determined by the first standard deviation and the mean together, and whether the health state evaluation parameter of a certain device is a suspicious parameter is determined.

For example, after the main contact resistance R _1n、R_2n、…、R_mn-1、R_mn of the same type of contactor n of the same type of vehicle (1-m) is obtained, the first mean R _0n and the first standard deviation σ _n corresponding to all the health evaluation parameters R _1n、R_2n、…、R_mn-1、R_mn may be determined, wherein the first mean R _0n and the first standard deviation σ _n corresponding to the same type of contactor n may be obtained by the following calculation formulas:

It should be noted that in step S22, that is, according to the first mean value and the first standard deviation corresponding to all the health status evaluation parameters, it is determined whether the health status evaluation parameters of each device in the same device are suspicious parameters, and in particular, the determination may be performed in a plurality of manners, where in some embodiments, according to the first mean value and the first standard deviation corresponding to all the health status evaluation parameters, it may be determined whether the health status evaluation parameters corresponding to each device are suspicious parameters based on the following criteria.

The first suspicious parameter diagnostic approach is based on the "3 sigma criterion":

That is, in step S22, that is, determining whether the health status evaluation parameter corresponding to each device is a suspicious parameter according to the absolute error and standard deviation corresponding to each device, specifically includes the following steps:

S221A: determining the absolute value of the difference between the health state evaluation parameters of all devices in the same type of devices and the first mean value to obtain first absolute errors corresponding to all devices;

After the first mean R _0n and the first standard deviation σ _n are calculated, the absolute value of the difference between the resistance value of the main contact of each contactor in the same type of contactor and the first mean R _0n can be determined, so as to obtain the first absolute error corresponding to each contactor, where the first absolute errors corresponding to each contactor are respectively: r _1n-R_0n|、|R_2n-R_0n|、…、|R_mn-R_0n.

S222A: determining the ratio of the first absolute error to the first standard deviation corresponding to each device;

S223A: determining a comparison coefficient according to the quantity of all health state evaluation parameters, wherein the comparison coefficient and the quantity are in positive correlation;

In this step, the comparison coefficient is further determined according to the number of all the health status evaluation parameters, and it should be noted that, based on the 3 sigma criterion, the comparison coefficient may be selected to be 3, but it is worth noting that the comparison coefficient may be configured according to the number of all the health status evaluation parameters, and the comparison coefficient and the number are in positive correlation, that is, the greater the number of all the health status evaluation parameters formed by the same device in the same device type, the higher the comparison coefficient. For example, when the number of health state evaluation parameters is greater than a certain number, the comparison coefficient may also be set to 4, which is not particularly limited and not described.

S224A: if the ratio of the first absolute error corresponding to the target device to the first standard deviation in each device is larger than or equal to the comparison coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter; if the ratio of the corresponding first absolute error to the first standard deviation is smaller than the comparison coefficient, determining that the health state evaluation parameter corresponding to the target device is not a suspicious parameter.

For example, when determining that the first absolute errors corresponding to the contactors are respectively: and R _1n-R_0n|、|R_2n-R_0n|、…、|R_mn-R_0n, and determining a ratio of the first absolute error corresponding to each contactor to the first standard deviation after determining the first standard deviation σ _n, that is, a ratio corresponding to each device:

after determining the comparison coefficient and the ratio of the first absolute error corresponding to each device to the first standard deviation, determining whether the health state evaluation parameter corresponding to each device is a suspicious parameter according to the relation between the comparison coefficient and the ratio of each device, specifically, setting the device to be tested as a target device, if the ratio of the corresponding first absolute error to the first standard deviation is greater than or equal to the comparison coefficient, determining the health state evaluation parameter corresponding to the target device as the suspicious parameter, and if the ratio of the corresponding first absolute error to the first standard deviation is less than the comparison coefficient, determining the health state evaluation parameter corresponding to the target device as the non-suspicious parameter.

For example, taking the proportionality coefficient as 3 as an example, the determination manner of the health state evaluation parameters corresponding to each device is as follows:

Let the device corresponding to R _1n be the target device if I.e., |r _1n-R_0n|≥3σ_n, determining R _1n as a suspicious parameter, wherein the device corresponding to R _1n is in an unhealthy state and possibly in a damaged state; otherwise, the device corresponding to R _1n is in a healthy state and is not in a damaged state.

Let the device corresponding to R _2n be the target device ifI.e., |r _2n-R_0n|≥3σ_n, determining R _2n as a suspicious parameter, wherein the device corresponding to R _2n is in an unhealthy state and possibly in a damaged state; otherwise, the device corresponding to R _2n is in a healthy state and is not in a damaged state.

Let the device corresponding to R _mn be the target device ifI.e., |r _mn-R_0n|≥3σ_n, determining R _mn as a suspicious parameter, wherein the device corresponding to R _mn is in an unhealthy state and possibly in a damaged state; otherwise, the device corresponding to R _mn is in a healthy state and is not in a damaged state;

The health status of each device is available for other ones of the devices and so forth, and will not be repeated here.

The second suspicious parameter diagnostic approach is based on the "glabros criterion":

that is, in step S22, that is, according to the first mean value and the first standard deviation corresponding to all the health status evaluation parameters, it is determined whether the health status evaluation parameters of each device in the same device are suspicious parameters, and the method specifically includes the following steps:

S221B: and determining the difference value between the health state evaluation parameters of all devices in the same type of devices and the first mean value to obtain the corresponding target error value of all devices.

After the first mean R _0n and the first standard deviation σ _n are calculated, the difference between the resistance of the main contact of each contactor in the same type of contactor and the first mean R _0n can be determined, so as to obtain the target error value corresponding to each contactor, where the target error values corresponding to each contactor are respectively: r _1n-R_0n、R_2n-R_0n、…、R_mn-R_0n.

S222B: and determining the ratio of the target error value corresponding to each device to the first standard deviation.

For example, after determining the target error values corresponding to the contactors are respectively: r _1n-R_0n、R_2n-R_0n、…、R_mn-R_0n, and after determining the first standard deviation sigma _n, may determine the ratio of the target error value corresponding to each contactor to the first standard deviation sigma _n, where the ratio is respectively:

S223B: if the ratio of the target error value corresponding to the target device to the first standard deviation is greater than or equal to the Grabbs critical value, determining that the health state evaluation parameter corresponding to the target device is a suspicious parameter, and if the ratio of the target error value corresponding to the target device to the first standard deviation is less than the Grabbs critical value, determining that the health state evaluation parameter corresponding to the target device is not a suspicious parameter.

In this step, a glabros threshold is first determined, which is related to the number of all health assessment parameters and the preset significance level coefficient α.

After the main contact resistance R _1n、R_2n、…、R_mn-1、R_mn of the same type of contactor n of the same type of vehicle (1-m) of the same lot type is obtained, R _1n、R_2n、…、R_mn-1、R_mn may be sequentially sorted by size and set to R ₍₁₎≦R₍₂₎≦…≦R_(m). According to the Grabbs criterion, can be derivedAndTaking the preset significance level coefficient α as 0.05 or 0.01, a glabros threshold table as shown in table 1 below can be obtained, where g ₀ (m, α) represents the glabros threshold, and m represents the number:

TABLE 1

As shown in table 1, the glaubes threshold value is related to the number m of all the health status assessment parameters and the preset significance level coefficient α, for example, when the number m of all the health status assessment parameters is 3 and the preset significance level coefficient α is 0.05, the corresponding glaubes threshold value g ₀ (m, α) is 1.15. The corresponding glabros threshold g ₀ (m, α) for various combinations of m and α can be obtained according to table 1 above, and are not illustrated here. It should be noted that, the preset significance level coefficient α is only exemplary, and the corresponding table 1 is not limited to the embodiment of the present invention. According to the health diagnosis precision, the magnitude of the preset significance level coefficient alpha can be flexibly set, so that a corresponding Grabbs critical value table is derived.

Thus, in this embodiment, the ratio of the target error value of the target device to the first standard deviation in each device is set toIf g _(i)≥g₀ (m, alpha), determining the health state evaluation parameter corresponding to the target device as a suspicious parameter; g _(i)<g₀ (m, α), determining that the health status evaluation parameter corresponding to the target device is not a suspicious parameter.

For example, taking the number of all health state evaluation parameters as 50 and the preset significance level coefficient α as 0.05 as an example, the corresponding glabros critical value g ₀ (50,0.05) =2.96, the corresponding health state evaluation parameter determination manner of each device is as follows:

Let the device corresponding to R _1n be the target device if Namely, determining R _1n as a suspicious parameter, wherein a device corresponding to R _1n is in an unhealthy state and possibly in a damaged state; otherwise, the device corresponding to R _1n is in a healthy state and is not in a damaged state.

Let the device corresponding to R _2n be the target device ifDetermining R _2n as a suspicious parameter, wherein the device corresponding to R _2n is in an unhealthy state and possibly in a damaged state; otherwise, the device corresponding to R _2n is in a healthy state and is not in a damaged state.

Let the device corresponding to R _mn be the target device ifDetermining R _mn as a suspicious parameter, wherein the device corresponding to R _mn is in an unhealthy state and possibly in a damaged state; otherwise, the device corresponding to R _mn is in a healthy state and is not in a damaged state.

In the embodiment of the present invention, besides the first and second ways of determining whether suspicious parameters exist in all health status evaluation parameters of the same device, other ways may be provided, as follows:

a third suspicious parameter diagnostic method is based on the "roman novos benchmark":

In one embodiment, that is, in step S20, it is determined whether there is a suspicious parameter among all the health status assessment parameters of the same device, including the following steps:

S21': and determining a second mean value and a second standard deviation corresponding to the remaining health state evaluation parameters in all the health state evaluation parameters.

The remaining health state evaluation parameters are all health state evaluation parameters except the health state evaluation parameters corresponding to the target device.

S22': and determining the absolute value of the difference value between the health state evaluation parameter corresponding to the target device and the second average value to obtain a second absolute error corresponding to the target device.

For steps S21 ' -S22 ', after acquiring the health status evaluation parameters of the same device of the same type of equipment, determining a second mean value R ' _0n and a second standard deviation σ ' _n corresponding to the remaining health status evaluation parameters in all the health status evaluation parameters, and determining the absolute value of the difference between the health status evaluation parameters corresponding to the target device and the second mean value R ' _0n to acquire a second absolute error corresponding to the target device. Assuming that the health state evaluation parameter corresponding to the target device is R _(j), after the R _(j) is removed, the second mean value R '_0n and the second standard deviation σ' _n corresponding to the other remaining health state evaluation parameters may be determined by the following calculation method:

wherein v _i＝(R_i-R′_0n

For example, assuming that the main contact resistance R _1n、R_2n、…、R_mn-1、R_mn of the same contactor n is m total resistance values, and assuming that the main contact resistance corresponding to the target device is R _1n, it is determined that the second average values R '_0n and σ' _n corresponding to the remaining health status assessment parameters except R _1n are the second standard deviations, that is, the second average value R '_0n and the second standard deviation σ' _n corresponding to R _2n、…、R_mn-1、R_mn.

And determining the absolute value of the difference between the health state evaluation parameter corresponding to the target device and the second average value to obtain a second absolute error corresponding to the target device, namely the absolute error corresponding to the target device is |R _j-R′_0n |.

S23': and determining the ratio of the second absolute error corresponding to the target device to the second standard deviation.

After determining the second absolute error R _j-R′_0n and the second standard deviation σ '_n corresponding to the target device, a ratio between the absolute error R _j-R′_0n and the standard deviation σ' _n corresponding to the target device may be determined, that is:

S24': if the ratio of the second absolute error corresponding to the target device to the second standard deviation is greater than or equal to the Romanofirpex test coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter; and if the ratio of the second absolute error corresponding to the target device to the second standard deviation is smaller than the Romanofirpex test coefficient, determining that the health state evaluation parameter corresponding to the target device is not a suspicious parameter.

In this step, the roman nofski test coefficient is determined first, and the roman nofski test coefficient is related to the number of all health status evaluation parameters and the preset significance level coefficient α, which may be set to 0.05 or 0.01.

After the main contact resistance R _1n、R_2n、…、R_mn-1、R_mn of the same type of contactor n is obtained, according to the roman nofski criterion and the preset significance level coefficient α, the roman nofski test coefficient table shown in the following table 2 is obtained, K ₀ (m, α) represents the roman nofski test coefficient, m represents the number, and table 2 is as follows:

TABLE 2

As shown in table 2, the roman nofski test coefficient is related to the number m of all health status assessment parameters and the preset significance level coefficient α, for example, when the number m of all health status assessment parameters is 10 and the preset significance level coefficient α is 0.05, the corresponding roman nofski test coefficient K ₀ (m, α) is 2.43. The corresponding roman novos test coefficients K ₀ (m, α) for various combinations of m and α can be obtained according to table 2 above, and are not illustrated here. It should be noted that, the preset significance level coefficient α is only exemplary, and the corresponding table 2 is not limited to the embodiment of the present invention. According to the health diagnosis precision, the magnitude of the preset significance level coefficient alpha can be flexibly set, so that a corresponding romannovis test coefficient table is derived.

Therefore, in this embodiment, the ratio of the second absolute error corresponding to the target device to the second standard deviation is set asIf K _(j)≥K₀ (m, alpha), determining the health state evaluation parameter corresponding to the target device as a suspicious parameter; k _(j)<K₀ (m, α), determining that the health status evaluation parameter corresponding to the target device is not a suspicious parameter.

For example, taking the number of all health state evaluation parameters as 10 and the preset significance level coefficient α as 0.05 as an example, the corresponding roman nofski test coefficient K ₀ (10, 0.05) =2.43, the corresponding health state evaluation parameter determination manner of each device is as follows:

It should be noted that, in the above-mentioned ways of determining suspicious parameters, the device corresponding to the resistance corresponding to the two ends may be used as the target device to perform diagnosis preferentially, that is, according to the above-mentioned way of determining the health status according to the glaubes critical value, the health status of the device corresponding to the maximum evaluation parameter and the health status of the device corresponding to the minimum evaluation parameter in all the health status evaluation parameters are determined first, and if the devices corresponding to the maximum and minimum are in the health status, the devices in the same batch are confirmed to be in the health status. If the device corresponding to the maximum value and/or the small value of the health state evaluation parameter is not in the health state, eliminating the health state evaluation parameter corresponding to the device in the health state, and continuously determining the health state of the rest devices according to the health state diagnosis mode until diagnosis is completed. It can be understood that the maximum value and the minimum value of the health state evaluation parameters are parameters which are most prone to coarse errors, so that in order to improve diagnosis efficiency, whether the maximum evaluation parameter and the minimum evaluation parameter are suspicious parameters or not can be determined first, if the maximum evaluation parameter and the minimum evaluation parameter are not suspicious parameters, other health state evaluation parameters which are remained in all health state evaluation parameters can be determined to be not suspicious parameters, and therefore all the devices in the batch are diagnosed to be in the health state, and unnecessary diagnosis work is reduced.

A fourth suspicious parameter diagnostic approach is based on the "dirk criterion":

For example, in this step, after the main contact resistance R _1n、R_2n、…、R_mn-1、R_mn of the same type of contactor n is obtained, R _1n、R_2n、…、R_mn-1、R_mn may be sequentially sorted by size, and set to R ₍₁₎≦R₍₂₎≦…≦R_(m). Then R ₍₁₎ is the maximum evaluation parameter and R _(m) is the maximum evaluation parameter.

For step b, it can be understood that, assuming that the dirk statistic formula corresponding to the maximum evaluation parameter is r _ij, and the dirk statistic formula corresponding to the minimum evaluation parameter is r' _ij, the following cases can be classified according to the number m of all health status evaluation parameters and dirk statistic criteria:

In this step d, a dirac threshold is determined, which is related to the number of all health status assessment parameters and the preset significance level coefficient a,

For example, assuming that the main contact resistance R _1n、R_2n、…、R_mn-1、R_mn of the same type of contactor n is set, and the preset saliency level coefficient α is set to 0.05 or 0.01, according to the dirk criterion and the preset saliency level coefficient α, a dirk threshold table shown in the following table 3 may be obtained, where R ₀ (m, α) represents the dirk threshold, and m represents the number, and table 3 is as follows:

TABLE 3 Table 3

As shown in table 3, the dirk threshold is related to the number m of all health state evaluation parameters and the preset significance level coefficient α, where the dirk statistic formula corresponding to the maximum evaluation parameter and the dirk statistic formula corresponding to the minimum evaluation parameter are different, for example, when the number m of all health state evaluation parameters is 10 and the preset significance level coefficient α is 0.05, the corresponding dirk threshold r ₀ (10, 0.05) is 0.477, and the dirk statistic formulas corresponding to the maximum and small evaluation parameters are respectively: And

It can be seen that, according to table 3 above, the corresponding dirk threshold r ₀ (m, α) for various combinations of m and α can be obtained, which is not illustrated here. It should be noted that, the value of the third preset significance level coefficient α and the corresponding table 3 are only exemplary and not limiting. According to the health diagnosis precision, the magnitude of the third preset significance level coefficient alpha can be flexibly set, so that the corresponding Dixon critical value is derived.

For example, taking the number of all health state evaluation parameters as 10 and the third preset significance level coefficient α as 0.05 as an example, the dirk threshold r ₀ (10, 0.05) is 0.477, and the determination manners of the health state evaluation parameters corresponding to the devices are as follows:

Since m=10, the dirk statistic formula corresponding to the maximum evaluation parameter is adopted If calculate, ifDetermining the maximum evaluation parameter as a suspicious parameter, wherein the device corresponding to the maximum evaluation parameter is in an unhealthy state and possibly in a damaged state; otherwise, the maximum evaluation parameter is not a suspicious parameter, and the device corresponding to the maximum evaluation parameter is in a healthy state and is not in a damaged state.

Since m=10, the dirk statistic formula corresponding to the minimum evaluation parameter is adoptedIf calculate, ifDetermining the minimum evaluation parameter as a suspicious parameter, wherein a device corresponding to the minimum evaluation parameter is in an unhealthy state and possibly in a damaged state; the minimum evaluation parameter is not a suspicious parameter, and the device corresponding to the minimum evaluation parameter is in a healthy state and is not in a damaged state.

After the step e is performed, if the maximum evaluation parameter and the minimum evaluation parameter are determined not to be suspicious parameters, determining that all other remaining health state evaluation parameters in all health state evaluation parameters are not suspicious parameters, if the maximum evaluation parameter and/or the minimum evaluation parameter are determined to be suspicious parameters, correspondingly eliminating the maximum evaluation parameter and/or the minimum evaluation parameter, continuously selecting new maximum evaluation parameter and/or minimum evaluation parameter, and re-performing the steps a-e until all suspicious parameters are determined, thereby determining whether all suspicious parameters in m health state evaluation parameters, and determining whether all devices are in health state.

It should be noted that in the above embodiment, a plurality of ways for determining whether the obtained suspicious parameters exist in the health status evaluation parameters of the same device are provided, so that the richness and the feasibility of the scheme are improved. It should be noted that the foregoing manner is merely an embodiment, and in practical application, after the health status evaluation parameters of the same device in the same type of device are obtained, other manners of determining whether there are suspicious parameters may be also used, which is not limited by the embodiment of the present invention. For example, other coarse error theory may be used in combination with abnormal parameters of the health assessment parameters of the same type of device to diagnose damaged or unhealthy devices.

A fifth suspicious parameter diagnostic method is based on the diagnostic principle of "limit health assessment parameters":

It should be noted that, when the number of the obtained health state evaluation parameters of the same device is smaller, for example, smaller than 3, a fifth suspicious parameter diagnosis mode may be further adopted, that is, the obtained health state evaluation parameters are directly compared with limit parameters, where the health state evaluation parameters are taken as the main contact resistance value of the contactor, the limit parameters are taken as the limit resistance of the contactor in the health range, and when the limit resistance is exceeded, it is indicated that the corresponding contactor is in an unhealthy state, possibly damaged, otherwise, in the health state, and not damaged.

It should be noted that, the present invention is a solution for performing health diagnosis after obtaining health status evaluation parameters of a plurality of devices of the same type, and it can be seen that the health status evaluation parameters of the same type have an effect on accuracy of subsequent diagnosis, and generally, the more data can refer to more information of diagnostic analysis, so, in order to improve diagnosis accuracy, in an embodiment, determining whether suspicious parameters exist in all health status evaluation parameters of the same type of devices includes:

It should be emphasized that, in this embodiment, different suspicious parameter diagnostic modes are selected based on the number of health status evaluation parameters, so as to improve the suspicious parameter diagnostic accuracy, and all suspicious parameter diagnostic modes are applicable to any number of health status evaluation parameters, that is, any number of health status evaluation parameters can be used, and the method is not particularly limited.

Specifically, diagnosing whether suspicious parameters exist in all the health state evaluation parameters according to the corresponding suspicious parameter diagnosis mode includes the following steps:

if the number is greater than a first preset number threshold, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices according to a first suspicious parameter diagnosis mode;

If the number is equal to the first preset number threshold, or is greater than the second preset number threshold and smaller than the first preset number threshold, determining whether suspicious parameters exist in all health state evaluation parameters of the same device according to a second suspicious parameter diagnosis mode;

If the number is equal to the second preset number threshold, or is greater than the third preset number threshold and smaller than the second preset number threshold, determining whether suspicious parameters exist in all health state evaluation parameters of the same device according to a third or fourth suspicious parameter diagnosis mode;

if the number is smaller than or equal to the third preset number threshold, determining whether suspicious parameters exist in all health state evaluation parameters of the same device according to a fifth suspicious parameter diagnosis mode.

The first, second, third, fourth and fifth preset quantity thresholds are preset values, and can be configured according to requirements, and the corresponding suspicious parameter diagnosis modes can be selected according to experience to be suitable calculation modes, and the quantity is m as shown in the following exemplary:

if m >50, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of devices according to the first suspicious parameter diagnosis mode;

If the m is less than or equal to 30 and less than or equal to 50, determining whether suspicious parameters exist in all health state evaluation parameters of the same device according to a second suspicious parameter diagnosis mode;

If 3<m is less than or equal to 30, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device according to a third or fourth suspicious parameter diagnosis mode;

If m <3, determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device according to a fifth suspicious parameter diagnosis mode.

It should be noted that, in an embodiment, two or more suspicious parameter diagnosis methods may be selected to determine whether suspicious parameters exist together, and when a certain health state evaluation parameter is considered as suspicious, a certain health state evaluation parameter is determined as suspicious. When the judging results of the methods are contradictory, discarding the diagnosis result, and re-acquiring the health state evaluation parameters of the devices of the same type to perform the health state evaluation process again. It can be appreciated that in the embodiment of the present invention, the comparison of several modes can be adopted, so as to avoid the error problem caused by adopting a single criterion, and further improve the accuracy of diagnosis.

In one embodiment, if there is no suspicious parameter, the method further comprises the steps of:

s50: comparing each health state evaluation parameter of all health state evaluation parameters with a corresponding limit parameter;

S60: if the health state evaluation parameter is larger than the corresponding limit parameter, determining that the device corresponding to the health state evaluation parameter is in an unhealthy state.

For example, if the contactor is diagnosed as unhealthy based on suspicious parameters, maintenance is required. Meanwhile, the ultimate resistance of the contactor in the healthy range is Rn, and when the resistance of the main contact of the contactor exceeds the ultimate resistance, the contactor is in an unhealthy state and needs to be reminded of maintenance. Therefore, in the embodiment of the invention, the health state of the contactor can be judged together based on the suspicious parameter diagnosis mode and the threshold judgment, so that the contactor is more accurate and effective.

It should be noted that, in an embodiment, when the number of all the health status evaluation parameters is large, any of the first to fourth suspicious parameter diagnosis methods may be used to determine the suspicious parameters, and finally, re-diagnosis is performed according to the limit parameters to diagnose whether a certain device is healthy. For example, if any one of the first to fourth suspicious parameter diagnosis modes is used to determine that the health state evaluation parameter a is a suspicious parameter, then the unhealthy device corresponding to the health state evaluation parameter a is primarily determined, then the health state evaluation parameter a is compared with the corresponding limit parameter B, if the health state evaluation parameter a is greater than the limit parameter B, then the unhealthy device corresponding to the health state evaluation parameter a is finally determined to be in an unhealthy state, possibly damaged, and maintenance or replacement is required, thereby effectively improving the accuracy of diagnosis.

In one embodiment, the device diagnostic method further comprises the steps of:

s70: and feeding back the device diagnosis result to the corresponding equipment so that the corresponding equipment displays the device diagnosis result.

Taking equipment as an example of a vehicle, the device diagnosis results can be fed back to the vehicle end calculation module of the corresponding vehicle, so that the vehicle end calculation module can display the diagnosis results through the whole vehicle instrument or the driver terminal equipment, and thus, a driver can know the health state of the device in real time, and when unhealthy devices exist, the device can be replaced in time, and the safety of the device is effectively improved.

Referring to fig. 7, referring to the following detailed description of a device diagnosis platform in the device diagnosis method of the present invention, a device diagnosis platform in an embodiment of the present invention includes:

an acquiring module 101, configured to acquire, in real time, a health status evaluation parameter of a device of a same type of equipment, where the health status evaluation parameter is associated with a current status of the device;

The determining module 102 is configured to determine whether suspicious parameters exist in all health state evaluation parameters of the same device, where the suspicious parameters are parameters having preset errors with other parameters of all health state evaluation parameters;

and the diagnosis module 103 is used for diagnosing that the device corresponding to the suspicious parameter is in an unhealthy state if the suspicious parameter exists.

In one embodiment, the determining module 102 is specifically configured to:

In one embodiment, if there are no suspicious parameters, the determining module 102 is further configured to:

In an embodiment, all health status evaluation parameters of the same device acquired by the acquisition module 101 are the temperature of the device, the resistance values of the two ends or the voltage difference of the two ends.

In an embodiment, all the health status evaluation parameters of the same device acquired by the acquisition module 101 are health status evaluation parameters acquired at the same time.

The invention provides a device diagnosis platform, which comprises the steps of acquiring health state evaluation parameters of the same type of devices of the same type of equipment in real time, wherein the health state evaluation parameters are related to the current state of the devices; determining the health state evaluation parameters of the same type of devices according to the health state evaluation parameters of each device; determining whether suspicious parameters exist in all health state evaluation parameters of the same type of device, wherein the suspicious parameters are parameters with preset errors with other parameters of all health state evaluation parameters; if the suspicious parameters exist, determining that the device corresponding to the suspicious parameters is in an unhealthy state. Therefore, the embodiment of the invention can acquire the health state evaluation parameters of the same device of different devices in the same type in real time to determine the health state of each device, so that the service life of the device can be diagnosed, rather than judging the health state of each device when the vehicle is regularly maintained and checked, the service life of each device can be monitored in real time, the problem of inaccurate data caused by single detection during regular maintenance can be effectively reduced, and the safety of the device is improved.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein. For specific limitations of the device diagnostic platform, reference may be made to the limitations of the device diagnostic method hereinabove, and no further description is given herein. The various modules in the device diagnostic platform described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a device diagnostic platform is provided, which may be a big data platform, the internal structure of which may be as shown in FIG. 8. The device diagnosis platform comprises a processor, a memory and a communication interface which are connected through a system bus. Wherein the processor of the device diagnostic platform is configured to provide computing and control capabilities. The memory of the device diagnostic platform includes volatile and non-volatile storage media, internal memory. The storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the storage media. The communication interface of the device diagnosis platform is used for realizing communication connection with external equipment, such as the BMS module in the vehicle in the previous embodiment, so that the BMS module can upload the health state evaluation information of the contactor to the device diagnosis platform through the communication interface. The computer program, when executed by a processor, implements a device diagnosis method, and may in particular correspond to the description of the previous method embodiments, which is not repeated here.

The embodiment of the invention also provides a readable storage medium, which stores a computer program, and the computer program realizes the steps of the device diagnosis method or the functions of the device diagnosis platform when being executed by a processor, and is not repeated here in detail.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.

The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A device diagnostic method comprising:

acquiring health state evaluation parameters of the same type of device of the same type of equipment in real time, wherein the health state evaluation parameters are related to the current health state of the device, the health state evaluation parameters are parameters of the monitoring state of the device, the device comprises a contactor, and the health state evaluation parameters are the temperature of the device, the resistance values of two ends or the voltage difference of two ends of the device;

if the suspicious parameters exist, diagnosing that the device corresponding to the suspicious parameters is in an unhealthy state;

wherein the determining whether suspicious parameters exist in all health state evaluation parameters of the same device includes:

Determining a corresponding suspicious parameter diagnosis mode according to the quantity and the size of all the health state evaluation parameters;

2. The method of claim 1, wherein determining whether suspicious parameters exist among all health assessment parameters of the same class of devices comprises:

Determining a first mean value and a first standard deviation corresponding to all the health state evaluation parameters;

3. The method according to claim 2, wherein determining whether the health status evaluation parameters of each device in the same device class are suspicious parameters according to the first mean and the first standard deviation corresponding to the health status evaluation parameters comprises:

Determining the absolute value of the difference between the health state evaluation parameter of each device and the first mean value to obtain a first absolute error corresponding to each device;

determining the ratio of the first absolute error corresponding to each device to the first standard deviation;

And if the ratio of the first absolute error corresponding to the target device to the first standard deviation in each device is greater than or equal to the comparison coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter.

4. The method according to claim 2, wherein determining whether the health status evaluation parameters of each device in the same device class are suspicious parameters according to the first mean and the first standard deviation corresponding to the health status evaluation parameters comprises:

determining the difference value between the health state evaluation parameter of each device and the first mean value to obtain a target error value corresponding to each device;

and if the ratio of the target error value corresponding to the target device to the first standard deviation in each device is greater than or equal to a Grabbs critical value, determining that the health state evaluation parameter corresponding to the target device is a suspicious parameter, wherein the Grabbs critical value is related to a preset significance level coefficient and the number of all health state evaluation parameters.

5. The method of claim 1, wherein determining whether suspicious parameters exist among all health assessment parameters of the same class of devices comprises:

Determining a second mean value and a second standard deviation corresponding to the remaining health state evaluation parameters in the all health state evaluation parameters, wherein the remaining health state evaluation parameters are all health state evaluation parameters except the health state evaluation parameters corresponding to the target device;

determining the absolute value of the difference value between the health state evaluation parameter corresponding to the target device and the second mean value to obtain a second absolute error corresponding to the target device;

Determining a ratio of a second absolute error corresponding to the target device to the second standard deviation;

and if the ratio of the second absolute error corresponding to the target device to the second standard deviation is greater than or equal to the Romanofaci test coefficient, determining the health state evaluation parameter corresponding to the target device as a suspicious parameter, wherein the Romanofaci test coefficient is related to a preset significance level coefficient and the quantity of all health state evaluation parameters.

6. The method of claim 1, wherein determining whether suspicious parameters exist among all health assessment parameters of the same class of devices comprises:

c. determining whether the statistical value is greater than or equal to a dirk threshold value corresponding to the statistical value, wherein the dirk threshold value is related to a preset significance level coefficient and the number of all health state evaluation parameters;

d. If the statistical value is greater than or equal to a dirk threshold value corresponding to the statistical value, determining a health state evaluation parameter corresponding to the extreme size value as a suspicious parameter, and eliminating the extreme size value from all the health state evaluation parameters;

e. and (3) repeating the steps a-e on the residual health state evaluation parameters with the extreme values removed until all suspicious parameters are determined.

7. The method of claim 1, wherein determining whether suspicious parameters exist among all health assessment parameters of the same class of devices comprises:

If the diagnosis results of more than two suspicious parameter diagnosis modes are contradictory, the health state evaluation parameters of each device are acquired again, and diagnosis of the suspicious parameters is carried out again.

8. The method of any of claims 1-7, wherein if the suspicious parameter is not present, the method further comprises:

comparing each health state evaluation parameter of the all health state evaluation parameters with a corresponding limit parameter;

and if the health state evaluation parameter is larger than the corresponding limit parameter, determining that the device corresponding to the health state evaluation parameter is in an unhealthy state.

9. The method according to any of claims 1-7, wherein all health assessment parameters of the same class of devices are health assessment parameters acquired at the same time.

10. A device diagnostic platform, comprising:

The device comprises a contactor, wherein the health state evaluation parameter is the temperature of the device, the resistance of two ends or the voltage difference of two ends;

The diagnosis module is used for diagnosing that a device corresponding to the suspicious parameter is in an unhealthy state if the suspicious parameter exists;

11. A device diagnostic system comprising a plurality of devices of the same type and a device diagnostic platform;

the plurality of devices of the same type are used for uploading health state evaluation parameters of the devices of the same type to the device diagnosis platform in real time;

the device diagnosis platform is used for realizing the device diagnosis method according to any one of claims 1 to 9 or realizing the function of the device diagnosis platform according to claim 10.

12. A readable storage medium storing a computer program, characterized in that the computer program when executed by a processor performs the steps of the device diagnostic method according to any one of claims 1 to 9 or the functions of the device diagnostic platform according to claim 10.