CN106672722B - Machinery diagnosis device, machinery diagnosis method and machinery diagnosis system - Google Patents

Abstract

The present invention provides a machine diagnosis device, a machine diagnosis method, and a machine diagnosis system, which can improve the detection accuracy of abnormality or failure by considering the influence of the operation state in the diagnosis of the operation state of the machine. A device diagnosis device for diagnosing the operating states of a plurality of devices includes: a similar device selection unit (6) that selects a device that is not to be diagnosed in the plurality of devices based on information related to the operating frequency of the plurality of devices. A machine similar to the diagnosis target machine in a machine; a reference data calculation unit (7), which generates a diagnosis for diagnosing the action state of the diagnosis target machine based on the action state of the machine outside the diagnosis target machine selected by the similar machine selection unit (6). The reference data; the judging unit (12), which compares the reference data calculated by the reference data calculation unit (7) with the operating state of the machine to be diagnosed, and determines whether the operating state of the machine to be diagnosed is abnormal.

Description

Machine diagnosis device, machine diagnosis method, and machine diagnosis system

technical field

The present invention relates to a machine diagnosis device, a machine diagnosis method and a machine diagnosis system for diagnosing the operating state of machines such as elevators.

Background technique

An elevator is a device that transports people up and down in a building, and is a necessary device in an urban space. Therefore, it is required that the elevator maintain a normal operating state. For this reason, it is necessary to diagnose the operating state of the elevator, reliably detect abnormalities or failures, and promptly deal with them. In this regard, conventional technologies described in Patent Document 1 and Patent Document 2 are known.

In the technique described in Patent Document 1, the presence or absence of abnormal operation of the electromagnetic brake is diagnosed based on the disturbance of the current flowing in the coil of the electromagnetic brake of the elevator. In this case, the diagnosis is performed after a certain period of time has elapsed after the elevator stops. Accordingly, the influence of the temperature rise of the coil on the detection value of the current disturbance is eliminated.

In the technology described in Patent Document 2, even if the elevator to be fault diagnosed is normal, when the measured value of the running performance deviates from the standard judgment value, the judgment value is independently set based on the measurement values of other elevators of the same specification.

In the technology described in Patent Document 1, the temperature rise of the coil fluctuates according to the operating frequency of the elevator, or the fall of the coil temperature over time is affected by the outside air temperature, so the coil temperature at the time of diagnosis is different. .

In addition, in the technique described in Patent Document 2, even if the specifications of the elevators are the same, the measured value of the operational performance is correlated with the operating frequency of the elevators.

In this way, in the diagnosis of the operating state of an elevator in the above-mentioned prior art, since the measured value or the judgment value is affected by the running state or the use environment of the elevator, in many cases these influences are taken into consideration to set the threshold for judgment, so that diagnosis can be performed safely. In such a case, actually, even if the measured value deviates within the normal range, inspection may be performed for confirmation, and there is a problem that this inspection reduces the convenience of the elevator user.

Patent Document 1: Japanese Patent Laid-Open No. 2000-351550

Patent Document 2: Japanese Patent Laid-Open No. 2005-170661

Contents of the invention

Therefore, the present invention provides an equipment diagnosis device, equipment diagnosis method, and equipment diagnosis system capable of improving detection accuracy of abnormalities or failures in consideration of the influence of operating conditions in diagnosing the operation status of equipment such as elevators.

In order to solve the above-mentioned problems, the device diagnosis device of the present invention diagnoses the operating states of a plurality of devices, and includes: a similar device selection unit that selects from among the plurality of devices outside the diagnosis target based on information related to the operating frequency of the plurality of devices. selecting a machine similar to a machine to be diagnosed among a plurality of machines; and a reference data calculation unit that generates an index for evaluating the state of operation of the machine to be diagnosed based on the operating state of machines other than the machine to be diagnosed selected by the similar machine selecting unit. Diagnosis reference data; a judging unit that compares the reference data calculated by the reference data calculation unit with the operating state of the machine to be diagnosed, and determines whether the operating state of the machine to be diagnosed is abnormal.

In addition, in order to solve the above-mentioned problems, the device diagnosis method of the present invention is a method of diagnosing the operating state of a plurality of devices, which selects a non-diagnosable device among the plurality of devices based on information on the operating frequency of the plurality of devices. A machine similar to a machine to be diagnosed among multiple machines generates reference data for diagnosing the operating state of the machine to be diagnosed based on the operating state of the selected machine not to be tested, and the generated reference data and the machine to be diagnosed Compare the operating status of the machine to determine whether there is any abnormality in the operating status of the machine to be diagnosed.

In addition, the appliance diagnosis system of the present invention diagnoses the operating states of a plurality of appliances, includes a plurality of monitoring terminal devices for obtaining information on the operating frequencies of the plurality of appliances, and includes a center device, the center device including: An appliance selection unit that selects an appliance that is similar to an appliance to be diagnosed among the appliances, from appliances other than an appliance to be diagnosed among the appliances, based on information on operating frequencies of the appliances acquired by the monitoring terminal device; reference data A calculation unit that generates reference data for diagnosing the operation state of the diagnosis target device based on the operation state of the non-diagnosis device selected by the similar device selection unit; and a determination unit that uses the reference data calculated by the reference data calculation unit Compared with the operation status of the equipment to be diagnosed, it is judged whether there is any abnormality in the operation status of the equipment to be diagnosed.

According to the present invention, it is possible to perform diagnosis in consideration of the operating state of the equipment, so the accuracy of diagnosis is improved. Therefore, a sign of abnormality can be detected, and maintenance can be performed at an appropriate timing, thereby reducing downtime due to maintenance and improving user convenience.

Problems, configurations, and effects other than those described above can be clarified by describing the following embodiments.

Description of drawings

Fig. 1 is a functional block diagram showing the configuration of an elevator diagnostic device according to the first embodiment.

FIG. 2 is operating frequency data estimated by a movement frequency estimator.

Figure 3 is table data used in the selection of similar elevators.

Fig. 4 is sensor data similar to an elevator and reference data calculated by a reference data calculator.

FIG. 5 is an example of the relationship between the sensor output value and the temperature in the reference data.

Fig. 6 is a functional block diagram showing the configuration of an elevator diagnostic device according to the second embodiment.

Fig. 7 is a block diagram of an elevator diagnostic device according to the third embodiment.

Explanation of symbols

1a, 1z: start times getter

2a, 2z: run time getter

3a, 3z: elevator specification saver

4a, 4b, 4z: Sensors

5a, 5b, 5z: Operation frequency estimator

6: Similar to elevator selector

7: Benchmark Calculator

8: Temperature fluctuation eliminator

9: Temperature sensitivity estimator

10: temperature data

11: Temperature fluctuation eliminator

12: Determiner

13: Exporter

14a, 14b, 14z: building use saver

21a, 21b: Building

22: Surveillance Center

30: Communication line network

31a, 31b: elevator

32a, 32b: monitoring terminal device

33: Center device

Detailed ways

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In each figure, the part with the same reference number represents the same structural element or the structural element which has a similar function.

(Example 1)

The present invention performs machine diagnosis in consideration of deviation or variation in measured values due to the operating state of the machine to be diagnosed or the influence of the use environment.

Fig. 1 is a functional block diagram showing the configuration of an elevator diagnostic device according to the first embodiment.

Input the data of the start times acquirer 1z, running time acquirer 2z, elevator specification saver 3z, and building use saver 14z of the elevator to be diagnosed into the operation frequency estimator 5z to estimate the run frequency of the elevator to be diagnosed.

In addition, for another elevator A that is of the same model as the elevator to be diagnosed but differs in some specifications (the number of floors, the number of passengers, and the speed of the car, etc.), the number of starts acquisition device 1a, the operation time acquisition device 2a, and the elevator specification are saved. Each data of the elevator 3a and the building use storage device 14a is input to the operation frequency estimator 5a, and the operation frequency of other elevators A is estimated. Similarly for elevator B, each data of the unillustrated start times acquirer, running time acquirer and elevator specification holder and the data of the building use holder 14a shown in FIG. 1 are input to the operation frequency estimator 5b, The operating frequency of another elevator B is estimated. In addition, the same applies to elevators A and B for non-diagnosed elevators (not shown) other than elevators A and B. FIG.

The operating frequency of the outside elevators A, B etc. estimated by the operating frequency estimator 5a, 5b etc., the operating frequency of the diagnostic object elevator estimated by the operating frequency estimator 5z are input to the similar elevator selector 6, A plurality or one non-diagnosed elevator (hereinafter referred to as "similar elevator") whose operating frequency is close to that of the diagnosed elevator is selected.

The reference data calculator 7 inputs the sensor data measured by the sensors 4a, 4b etc. of the operating states (for example, the coil current of the electromagnetic brake) of the elevators A, B etc. outside the object of diagnosis, the output of the similar elevator selector 6 and the similar elevator related data. Then, the reference data calculator 7 selects sensor data of a similar elevator from the input sensor data of an elevator outside the diagnosis object, and calculates a reference as a judgment criterion when diagnosing the operation state of the diagnosis object elevator based on the selected sensor data. data.

The reference data calculated by the reference data calculator 7 is input to the temperature sensitivity estimator 9 . In addition, the temperature data 10 is also input to the temperature sensitivity estimator 9 . Based on these data, the temperature sensitivity estimator 9 estimates the temperature sensitivity similar to the reference data of an elevator.

The data of the sensor 4z of the elevator to be diagnosed, the air temperature data 10, and the temperature sensitivity data estimated by the temperature sensitivity estimator 9 are input to the temperature fluctuation eliminator 11, and based on these data, the temperature fluctuation eliminator 11 eliminates the temperature influence on the elevator to be diagnosed . In addition, the reference data calculated by the reference data calculator 7, the temperature data 10, and the temperature sensitivity estimated by the temperature sensitivity estimator 9 are input to the temperature variation canceller 8, and based on these data, the temperature variation canceller 8 cancels the reference data. temperature fluctuations.

The reference data from which the temperature variation has been eliminated by the temperature variation canceller 8 and the sensor data of the elevator to be diagnosed from which the temperature variation has been eliminated by the temperature variation canceller 11 are input to the determiner 12 . Based on these data, the determiner 12 determines whether the sensor data of the elevator to be diagnosed is within a predetermined range with respect to the reference data, and outputs the result to the output unit 13 . The output unit 13 displays the determination result output by the determination unit 12 in a predetermined form (image, character, sound, etc.).

In FIG. 1, elevator specification storage (3z, 3a, etc.) and building use storage (14z, 14a, 14b, etc.) are constituted by storage devices such as semiconductor memories. In addition, each functional block other than the sensor and the temperature data is operated as each functional block by executing a predetermined program by a computer system including an arithmetic processing device such as a personal computer.

Next, the processing content of the operation frequency estimator (5z, 5a, 5b, etc.) is demonstrated using FIG.

Fig. 2 is the operation frequency data estimated by the movement frequency estimator in the first embodiment, showing the relationship between the number of times the elevator is started and the operation time. In addition, in FIG. 2 , the operation frequency data estimated by a plurality of operation frequency estimators are collectively shown.

The operating frequency of the elevator is related to indicators such as the start time, the number of starts, the specification of the elevator, and the use of the building (the method of using the elevator corresponding to the building). First, indicate the relationship between the number of starts and the running time according to the purpose of the building and the specifications of the elevator. The data of building usage, elevator specifications, number of starts, and running time are respectively obtained from the building usage storage device (14z, 14a, 14b, etc.), the elevator specification storage device (3z, 3a, etc.), the number of startup times acquisition device (1z, 1a, etc. ) and runtime getter (2z, 2a, etc.) inputs.

In FIG. 2 , a plurality of markers are divided into marker groups located on the same line, that is, groups a, b, and c in which the relationship between the number of times of activation and the operating time is the same. Here, the groups a, b, and c represent differences in elevator specifications. Even for the same run time, the number of starts is different for each group. For example, an elevator that shuttles to the top floor of a skyscraper has fewer starts than an elevator that travels 2 floors for the same running time. In addition, although it is not clearly described in FIG. 2, even within the same group, the variation range of the indication differs depending on the use of the building. In addition, FIG. 2 is an example of grouping based on the number of times elevators are started and the running time, but grouping may be performed by one index such as only the number of times elevators are started or only the running time. The more metrics that are used for frequency grouping, the more granular the grouping can be.

Such grouping, that is, classification is performed because it is considered that the sensor data of the sensor 4z of the elevator to be diagnosed is affected by the operating frequency, specifically, the number of starts per unit time. For example, since the temperature of the coil fluctuates due to the number of starts per unit time, the sensor data of the coil current of the electromagnetic brake is affected by the maximum number of starts per unit time. Taking this into consideration, similar to the elevator selector 6, an elevator outside the diagnosis object whose operating frequency and use method is the same as that of the diagnosis object elevator is selected as a comparison object suitable for reliably diagnosing the diagnosis object elevator.

Next, the content of processing similar to the elevator selector 6 will be described using FIG. 3 .

Figure 3 shows table data used in the selection of similar elevators. Regarding this table data, it is based on the operation frequency data (including information related to elevator specifications (groups a, b, c) in Fig. 2) input from the operation frequency estimator (5z, 5a, 5b, etc.), and from The building use data input by the building use savers (14z, 14a, 14b, etc.) is created and updated by the elevator selector 6 similarly.

In the table data shown in FIG. 3, groups a, b, and c are the elevator specifications, indicators α, β, and γ representing the operating frequency, and building uses 1, 2, and 3 (recorded as Roman numerals in FIG. 3 ). As a parameter, a plurality of elevators that can be diagnostic targets are classified into any one of the groups (1) to (9). In addition, in this FIG. 3 , three combinations of groups (standards) and indexes are shown, but cases where the indexes of the same group are different, or cases where indexes of different groups are the same are also included. For example, in FIG. 3 , (a, α) is shown as a combination of a group (standard) and an index, and (a, β) may also be included.

Here, the operation frequency data input from the operation frequency estimator is converted into an index according to a formula or a conversion rule previously set in the similar elevator selector 6 . For example, for brakes, the maximum number of starts per unit time becomes the main indicator. In addition, indicators are represented by specific values or ranges of values.

Similar to the elevator selector 6, the operation frequency data input from the operation frequency estimator 5z of the elevator to be diagnosed is converted into an index, and the index after conversion is extracted from the table data in FIG. Elevators corresponding to the building use data, and output the extracted elevators as similar elevators. In Fig. 3, the non-diagnosed elevator belonging to group (4) is selected as a similar elevator, and the index of this group (4) and the building use are converted from the index β (corresponding group ( The specification) is the same as b) and the building use 1 of the elevator to be diagnosed.

In addition, the building use is used in the movement frequency estimation, and the building use is used again in order to select a similar elevator because the periodical change (for example, seasonal change) of the elevator operation state accompanying the building use is taken into consideration. For example, when the number of elevator users decreases during a long vacation, the operating state of the elevator changes. Therefore, as similar elevators to be compared, it is preferable to use elevators in the same building.

Next, the processing contents of the reference data calculator 7 will be described using FIG. 4 .

FIG. 4 shows sensor data like an elevator and reference data calculated by the reference data calculator 7 .

The reference data calculator 7 organizes the respective sensor data of the elevators (A, B, C, etc.) judged to be similar elevators in time series. Thereby, as shown in FIG. 4, the data which shows the time change of sensor data is obtained for every similar elevator. Based on these data, the reference data calculator 7 calculates a reference (value) and a deviation in the same timing, ie, the same time range, as reference data. At this time, as a reference value, the average value, median value, maximum value, minimum value, etc. of the sensor data of similar elevators (A, B, C, etc.) are used, corresponding to the operating state of the elevator that determines whether there is abnormality based on the sensor data ( For example, the type of the coil current of the electromagnetic brake, the elevator door switch signal, etc.) uses an index suitable for obtaining a reliable judgment result. In addition, as the deviation, an index indicating the magnitude of the data deviation, such as a standard deviation or an integer multiple thereof, is used.

The reference data calculator 7 calculates the reference data based on the data of the same time range among the time-series sensor data. This is because even for similar elevators with the same operating frequency, the acquisition time of the sensor data differs depending on the installation period and the like. The range is different, or if the time range is different, the influence of the temperature change on the elevator is different. In addition, the time interval for arranging the sensor data in time series is set in advance in the sensor, and can be appropriately set to one day, one week, one month, or the like.

Next, the processing contents of the temperature sensitivity estimator 9 will be described using FIG. 5 .

FIG. 5 shows an example of the relationship between the sensor output value and the temperature in the reference data.

The temperature sensitivity estimator 9 obtains, for example, the relationship between sensor output and temperature as shown in FIG. 5 based on the reference data and the air temperature data 10 . The air temperature data 10 can be measured by a temperature sensor provided by the building or the elevator, or weather data of the area where the building or the elevator is located can also be used. As shown in FIG. 5 , if there is a correlation between the temperature and the sensor output, the temperature sensitivity estimator 9 estimates the sensor sensitivity with respect to the temperature based on the correlation. In FIG. 5 , the inclination of the straight line obtained when each indicated point is approximated by a straight line corresponds to the sensor sensitivity (temperature sensitivity) with respect to temperature. The temperature sensitivity estimator 9 uses a pre-conceived calculation method corresponding to the correlation between the sensor output and the temperature. For example, if it is a linear (linear function) correlation as shown in FIG. Estimated temperature sensitivity.

Next, the processing contents of the temperature variation cancellers 8 and 11 will be described.

The temperature variation canceller 8 estimates the reference data calculated by the reference data calculator 7 based on the temperature sensitivity of the sensor data estimated by the temperature sensitivity estimator 9 and the air temperature data 10 (obtained from the temperature sensitivity estimator 9 together with the reference data). The temperature variation components included in the reference data and eliminate the estimated variation components from the reference data. In addition, the temperature variation canceller 11 similarly removes the temperature variation component from the sensor data of the elevator to be diagnosed. For example, in the case of a correlation relationship of a linear function as shown in FIG. 5 , if the temperature sensitivity (the inclination of the approximate line) is α, and the reference temperature (air temperature) is T ₀ , the sensor data S(T ) relative to the reference temperature is expressed as α(T-T ₀ ). Therefore, if α(T-T ₀ ) is subtracted from S(T), the temperature fluctuation component is eliminated from the sensor data (reference data, sensor data of the elevator to be diagnosed).

By eliminating the temperature component from the reference data and the sensor data of the elevator to be diagnosed using the temperature variation cancellers 8 and 11 in this way, it is possible to eliminate the difference in temperature influence between the two data in the determination by the determiner 12 described later. Therefore, determination accuracy can be improved.

In addition, when the temperature dependence of the physical quantity represented by the sensor data is small, the temperature sensitivity estimator 9 and the temperature fluctuation cancellers 8 and 11 may be omitted.

Next, the processing contents of the determiner 17 will be described.

The determiner 12 determines whether the sensor data of the elevator to be diagnosed whose temperature fluctuation has been removed by the temperature fluctuation remover 11 is within a predetermined judgment reference range relative to the reference data whose temperature fluctuation has been removed by the temperature fluctuation remover 8 . For example, the determiner 17 calculates the square error at each time point with respect to the sensor data of the elevator to be diagnosed, the time-series data having the measurement time information and the time-series data having the reference data of the time information, and calculates each calculated square error. The average of the errors, or mean squared error. If the calculated mean square error is below the predetermined judgment reference value, then the determiner 12 judges that the diagnosis content related to the sensor data of the elevator to be diagnosed is normal, and if the calculated mean square error is greater than the predetermined judgment reference value, then judged to be abnormal. Then, the determiner 12 outputs the result of the determination to the output unit 13 .

The output unit 13 displays the determination result output by the determination unit 12 , that is, information on the presence or absence of an abnormality, using images, characters, sounds, and the like. At this time, the sensor data and reference data of the elevator to be diagnosed may be displayed together with information on the presence or absence of an abnormality. At this time, the determiner 12 outputs the determination result to the output unit 13 , and transmits sensor data and reference data used for the determination to the output unit 13 . Accordingly, it is possible to reliably investigate the cause of the abnormality.

As described above, according to the first embodiment, the operating state of the elevator to be diagnosed is diagnosed based on the operation state of an elevator outside the diagnosis object whose operating frequency is similar to that of the elevator to be diagnosed, so that the accuracy and reliability of the diagnosis of the operation state are improved. . In addition, a similar elevator is selected according to the specifications and usage of the elevator, thereby setting reference data suitable for the elevator to be diagnosed, so that the accuracy and reliability of the diagnosis are improved. In particular, even if the normal range of the operating state changes according to the method of use, or when there is a lack of or no actual use record like a newly installed elevator, it is not necessary to perform the operation state confirmation work for setting the judgment standard, and the Criteria for judging the presence or absence of abnormalities with certainty.

In addition, according to the first embodiment, since the temperature fluctuation component is eliminated from the sensor data and the reference data indicating the operation state of the elevator to be diagnosed, even when the operation state changes due to air temperature or the temperature of the use environment, it is possible to perform Diagnostics with high precision and reliability.

In addition, the device diagnosis method of the first embodiment can be applied to various elevator devices such as brake devices and door opening and closing devices in elevators. In addition, the equipment diagnosis method of the first embodiment is not limited to elevator equipment, and can be applied to various equipment for diagnosing operating states such as air conditioners, refrigerators, home appliances, construction equipment, and factory equipment.

(Example 2)

Fig. 6 is a functional block diagram showing the configuration of an elevator diagnostic device according to Embodiment 2 of the present invention. Hereinafter, points different from Embodiment 1 will be mainly described.

In the present embodiment 2, the difference from the embodiment 1 is that, regarding the input data to the operation frequency estimator (5z, 5a, 5b, etc.), the number of starts, operation time, elevator specification and The elevator specification and building use in the building use, the number of starts and the running time are not used as input data. This is because elevators with the same building use and elevator specifications are generally used in the same way, so the number of starts and operating hours are considered to be the same.

In this embodiment 2, the operation frequency estimator (5z, 5a, 5b, etc.) obtains the elevator specification data and Building usage data, sorting these data is input to like elevator selector 6 as operation frequency data. The similar elevator selector 6 uses the elevator specification data and building usage data as parameters, and uses table data classifying elevators to select an elevator not to be diagnosed that matches the elevator specification data and building usage data of the elevator to be diagnosed.

According to the second embodiment, by using the operation state of the non-diagnosis elevator whose specification and building use is similar to the diagnosis object elevator as a reference, it is possible to substantially compare the operation state of the diagnosis object elevator with the operating frequency similar to the diagnosis object elevator. as a benchmark. Therefore, as in the first embodiment, the diagnostic accuracy and reliability of the operating state are improved. In addition, since the operation frequency estimator and the processing in elevator selection are simplified, the speed of diagnosis is improved.

In addition, the equipment diagnosis method of the second embodiment is the same as that of the first embodiment, and can be applied to various elevator machines, and is not limited to elevator machines, but can be applied to various machines for operating state diagnosis.

In Embodiment 1 and Embodiment 2 as described above, when the number of starts per unit time can be actually measured, the actual measured value of the number of starts per unit time can be used as the operating frequency to select a similar elevator. In addition, when various data for selecting similar elevators can be measured, the measured data can also be used.

In addition, in Fig. 1 and Fig. 6 , one sensor is shown for each elevator, but it is not limited thereto, and there may be a plurality of sensors. In this case, the sensor data of the similar elevator corresponding to the sensor data of the elevator to be diagnosed may be selected after determining the similar elevator. In addition, when the physical phenomenon that is the main cause of each sensor data is different, not only the sensor data that determines that the parameters of similar elevators match, but also sensor data that includes parameters within a predetermined allowable range can be used to select similar elevators.

(Example 3)

Fig. 7 shows a configuration diagram of an elevator diagnosis system according to Embodiment 3 of the present invention.

In the buildings 21a, 21b, elevators 31a, 31b are installed, respectively. Monitoring terminal devices 32a, 32b are connected to the elevator control devices of the elevators 31a, 31b (not shown), respectively. Based on the signal from the elevator control device, the monitoring terminal device acquires and stores information related to the operating frequency of the elevator, for example, the number of starts and the operating time. Moreover, the monitoring terminal device acquires and stores the sensor data which shows the operating state of an elevator output from the sensor (not shown) with which each elevator is equipped. In addition, the monitoring terminal device stores elevator specification data and building use data of the elevator.

In addition, a central device 33 for monitoring the operation status of a plurality of elevators (31a, 31b in FIG. 7 ) is installed in the monitoring center 22 geographically remote from the location of the buildings 21a, 21b.

The monitoring terminal devices 32a, 32b periodically or according to the command signal sent from the central device 33 via the communication line network 30, store the information related to the operating frequency of the elevator, the elevator specification data, the building use data of the elevator, and the sensor data via The communication line network 30 sends to the center device 33 .

The central device 33 generates and updates the table data for selecting similar elevators shown in FIG. In the same manner as in Example 1 and Example 2, an elevator outside the diagnosis object that is similar to the elevator in the diagnosis operation state is selected to calculate the reference data, and the reference data is compared with the sensor data indicating the operation state of the diagnosis object elevator to determine the operation of the diagnosis object elevator Whether the status is abnormal.

As a more specific device structure, the monitoring terminal device is equipped with information acquisition units related to the operating frequency of the elevator shown in Figure 1 and Figure 2 (starting frequency acquisition device (Figure 1), running time acquisition device (Figure 1), elevator Specification storage (Fig. 1, Fig. 2), building use storage (Fig. 1, Fig. 2)) and operation frequency estimator, and equipped with sensor data acquirer and sensor data not shown in Fig. 1, Fig. 2 Saver. Moreover, the center apparatus 33 is equipped with the similar elevator selector shown in FIG. 1, FIG. In addition, a movement estimator can also be provided at the central device.

According to this embodiment, as in the first and second embodiments, the diagnostic accuracy and reliability of the operating state are improved. In addition, the operating states of a plurality of elevators installed at geographically distant positions can be collectively diagnosed by the center device.

In addition, the equipment diagnosis system of the third embodiment is not limited to elevators, but can be applied to various equipment for performing operation state diagnosis.

In addition, this invention is not limited to the said Example, Various modification examples are included. For example, the above-mentioned embodiments are described in detail for the purpose of explaining the present invention intelligibly, but are not necessarily limited to having all the configurations described. In addition, it is possible to add/delete/replace a part of the structure of each embodiment with another structure.

Claims (11)

1. a kind of machinery diagnosis device, diagnoses the action state of multiple machines, which is characterized in that have：

Run frequency scavenging valve, run time, the number of starts, the specification of machine based on machine, at least appointing on the way Meaning one, to estimate operation frequency；

Similar machine selecting unit, based on the relevant information of operation frequency with the multiple machine, from the multiple The machine similar with the diagnosis subject machine in the multiple machine is selected in the outer machine of diagnosis object in machine；

Reference data computing unit, based on outside the diagnosis object selected by the similar machine selecting unit machine it is dynamic Make state, to generate the reference data diagnosed for the action state to the diagnosis subject machine；And

Judging unit, the reference data that the reference data computing unit is calculated and the diagnosis subject machine are moved It is compared as state, to judge that it is without exception that the action state of the diagnosis subject machine has,

The purposes of the similar machine selecting unit based on machine, to select the similar machine.

2. machinery diagnosis device according to claim 1, which is characterized in that

Sensor with the action state that can measure the diagnosis subject machine,

The reference data computing unit generates the reference data based on the sensing data of the information with measurement time.

3. machinery diagnosis device according to claim 1, which is characterized in that

The reference data computing unit based on it is multiple it is described diagnosis objects outside machine phases in the same time range action state come Generate the reference data.

4. machinery diagnosis device according to claim 1, which is characterized in that

Have temperature variation and eliminate unit, it is related to action state based on temperature data, temperature which eliminates unit Relationship eliminates temperature variation ingredient from the reference data and the action state for diagnosing subject machine,

The judging unit compares eliminates the base value after unit eliminates temperature variation ingredient by the temperature variation According to this and it is described diagnosis subject machine action state, come judge it is described diagnosis subject machine the action state be no different Often.

5. machinery diagnosis device according to claim 4, which is characterized in that

The temperature data is obtained from weather information.

6. machinery diagnosis device according to claim 4, which is characterized in that

The temperature data is obtained by temperature sensor.

7. machinery diagnosis device according to claim 4, which is characterized in that

Has temperature control scavenging valve, which is based on the reference data and the temperature number According to estimate the correlativity.

8. machinery diagnosis device according to claim 1, which is characterized in that

Have multiple sensors, multiple sensor detects the action state of the diagnosis subject machine and the diagnosis The action state of the outer machine of object.

9. machinery diagnosis device according to claim 1, which is characterized in that

The multiple machine is multiple elevators.

10. a kind of machinery diagnosis method, diagnoses the action state of multiple machines, which is characterized in that

Run time, the number of starts, the specification of machine based on machine, on the way at least any one, to estimate operation frequency Degree,

Based on the relevant information of operation frequency with the multiple machine, machine outside the diagnosis object out of the multiple machine The machine similar with the diagnosis subject machine in the multiple machine is selected in device,

Based on the action state of machine outside the diagnosis object selected, to generate for being moved to the diagnosis subject machine The reference data diagnosed as state,

The action state of the reference data of generation and the diagnosis subject machine is compared, to judge described examine The action state of disconnected subject machine have it is without exception,

Purposes based on machine, to select the similar machine.

11. a kind of machinery diagnosis system, diagnoses the action state of multiple machines, which is characterized in that have：

Multiple monitoring terminal devices obtain the relevant information of operation frequency with the multiple machine；And

Center fixture,

The center fixture has：

Run frequency scavenging valve, run time, the number of starts, the specification of machine based on machine, at least appointing on the way Meaning one, to estimate operation frequency；

Similar machine selecting unit, the operation frequency with the multiple machine obtained based on the monitoring terminal device Relevant information, selection and the diagnosis object in the multiple machine from machine outside the diagnosis object in the multiple machine Machine as machine type；

Reference data computing unit, based on outside the diagnosis object selected by the similar machine selecting unit machine it is dynamic Make state, to generate the reference data diagnosed for the action state to the diagnosis subject machine；And

Judging unit, the reference data that the reference data computing unit is calculated and the diagnosis subject machine are moved It is compared as state, to judge that it is without exception that the action state of the diagnosis subject machine has,

The purposes of the similar machine selecting unit based on machine, to select the similar machine.