CN118688632B - A push rod motor performance detection system and method based on data analysis - Google Patents

Abstract

The application discloses a push rod motor performance detection system and method based on data analysis, which belongs to the technical field of motor testing, and comprises the following steps: receiving static data and dynamic data in the running process of the push rod motor, and carrying out homomorphic treatment on the static data and the dynamic data after the receiving is completed; after homomorphic processing is carried out on static data and dynamic data, abnormal data are removed, and the actual running state of the push rod motor is judged; and converting and sending the running state judgment result of the push rod motor to the user side to finish the detection of the push rod motor. In the implementation process of the application, the static data and the dynamic data in the running process of the push rod motor are received, and the static data and the dynamic data are homomorphically processed after the receiving is completed, so that the running state of the push rod motor is comprehensively judged according to the static data and the dynamic data, the running state of the push rod motor is sent to the display equipment of the user side, and meanwhile, the corresponding time stamp is attached, thereby being convenient for maintenance after detection.

Description

A push rod motor performance detection system and method based on data analysis

Technical Field

The present application relates to the technical field of motor testing, and specifically to a push rod motor performance detection system and method based on data analysis.

Background Art

A push rod motor, also called an electric push rod, an electric cylinder or a linear actuator, is a type of power installation engineering drive machine that can convert the rotational motion of a motor into linear motion and is widely used in various automation control systems.

The push rod motor needs to be tested for performance before leaving the factory. It will not be put on the market until it is determined to meet the factory standards. When leaving the factory, various performance testing devices will be used to put the push rod motor to be tested into the performance testing device to achieve performance testing of the push rod motor. For example, Chinese utility model patent with publication number CN212932879U discloses a push rod motor life testing device, which can be used to test the service life of the push rod motor.

However, after the push rod motor is installed in the applied equipment, when its performance needs to be tested, it needs to be removed according to the existing method, and then the corresponding equipment can be used for testing. This testing method will undoubtedly increase the workload, and ordinary consumers do not have the ability to disassemble it for testing. After installation, how to perform performance testing on the push rod motor without disassembling it and provide timely feedback to the user is a problem that needs to be solved urgently.

Therefore, it is necessary to provide a push rod motor performance detection system and method based on data analysis to solve the above problems.

It should be noted that the above information disclosed in this background technology section is only for understanding the background technology of the present application concept, and therefore, it may contain information that does not constitute the prior art.

Summary of the invention

Based on the above problems existing in the prior art, the problem to be solved by the present application is: to provide a push rod motor performance detection system and method based on data analysis, so as to achieve the effect of being able to perform homomorphic processing on static data and dynamic data and judge the operating status of the push rod motor.

The technical solution adopted by the present application to solve the technical problem is: a method for testing the performance of a push rod motor based on data analysis, the testing method comprising:

The server receives static data and dynamic data during the operation of the push rod motor, which are collected by the collection device, and performs homomorphic processing on the static data and dynamic data after receiving them;

After the static data and dynamic data are processed in the same way, the abnormal data is eliminated and the actual operating status of the push rod motor is judged;

The operation status judgment result of the push rod motor is converted and sent to the user end to complete the detection of the push rod motor.

During the implementation of the technical solution of the present application, static data and dynamic data are received during the operation of the push rod motor, and the static data and dynamic data are processed homomorphically after the reception is completed, so that the operating status of the push rod motor is comprehensively judged based on the static data and the dynamic data, and the operating status of the push rod motor is sent to the display device at the user end, and a corresponding timestamp is attached to facilitate maintenance after detection.

Furthermore, performing homomorphic processing on static data and dynamic data further includes the following steps:

Preprocess static data and dynamic data to extract static feature data and dynamic feature data;

Establish a static model based on known parameters and features and a dynamic model based on time series data respectively, and import the extracted static feature data and dynamic feature data into the corresponding models;

Receiving output results of a static model and a dynamic model, wherein the static model outputs an operating state of the push rod motor, and the dynamic model outputs a change prediction of the push rod motor based on a time series;

The threshold judgment method is used to judge the change prediction based on the time series of the dynamic model output, and the operation status prediction of the push rod motor based on the time series is analyzed.

Furthermore, the static model is established based on known parameters and features, and the dynamic model is established based on time series data. The static model and the dynamic model have at least one data input terminal and a data output terminal. The data input terminal is used to input static feature data and dynamic feature data, and the data output terminal is used to output results based on the input information.

Furthermore, the threshold determination method comprises the following steps:

The server obtains a dynamic parameter change value set of the push rod motor factory inspection, and splits the dynamic parameter change value set;

Compare the push rod motor change prediction result based on the time series output by the dynamic model with the input dynamic parameter change value set to obtain a comparison result. The comparison method can be difference comparison or ratio comparison;

The difference threshold and ratio threshold of the corresponding parameters are set, and the comparison result is judged with the difference threshold or the ratio threshold, and the operation status of the push rod motor with a time series is output.

Furthermore, judging the actual operating state of the push rod motor includes:

A time synchronization model is established, wherein the time synchronization model has two input terminals, wherein the two input terminals respectively input an output result of the static model and an output result of the dynamic model;

Synchronize and calibrate the time synchronization model so that the static model and the dynamic model can collect and output data based on the same timestamp;

Set a fixed calibration cycle and perform repeated calibration within the calibration cycle;

Monitoring the synchronization status of the time synchronization model during its operation;

After time synchronization, the operating state in which the static model output result is the same as the dynamic model output result at the same timestamp is used as the operating state of the push rod motor.

Furthermore, the homomorphic processing refers to processing different types of collected data to form data that can be processed using the same method and have the same computational complexity.

Furthermore, static data is preprocessed by using statistical features or feature engineering methods to extract key static features, and dynamic data is preprocessed by using time series analysis or frequency domain analysis methods to extract dynamic features with time series.

Furthermore, the output ends of the static model and the dynamic model are provided with logic circuits or state machines to output the data results in the form of binary signals.

Furthermore, the binary signal 1 indicates that the push rod motor operates normally, and the binary signal 0 indicates that the push rod motor operates abnormally.

A push rod motor performance detection system based on data analysis, the system comprising:

The data receiving and homomorphic processing module is used for the server to receive static data and dynamic data during the operation of the push rod motor. The static data and dynamic data are collected by the collection device, and the static data and dynamic data are processed homomorphically after receiving.

The actual operation state judgment module is used to judge the actual operation state of the push rod motor after isomorphic processing of static data and dynamic data;

The detection result display module is used to convert the operation status judgment result of the push rod motor and send it to the user end to complete the detection of the push rod motor.

The beneficial effect of the present application is: the present application provides a push rod motor performance detection system and method based on data analysis, which receives static data and dynamic data during the operation of the push rod motor, and performs homomorphic processing on the static data and dynamic data after the reception is completed, so as to comprehensively judge the operating status of the push rod motor based on the static data and the dynamic data, and send the operating status of the push rod motor to the display device at the user end, and at the same time attach a corresponding timestamp to facilitate maintenance after detection.

In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail with reference to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

FIG1 is a schematic diagram of the overall process of a method for testing the performance of a push rod motor based on data analysis in the present application;

FIG. 2 is a schematic diagram of the module structure of a push rod motor performance detection system based on data analysis in the present application.

DETAILED DESCRIPTION

It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

Embodiment 1: As shown in FIG1 , the present application provides a method for testing the performance of a push rod motor based on data analysis. The method is applied to the performance testing of the push rod motor, and the performance of the installed push rod motor is tested. The test results are fed back to the user end in a timely manner, so that the user can conveniently understand the current performance status of the push rod motor. The method includes the following steps:

Step 11: The server receives static data and dynamic data during the operation of the push rod motor, which are collected by the collection device, and performs homomorphic processing on the static data and dynamic data after receiving;

The operation process of the push rod motor requires the use of data collection equipment for data collection, where the collected data includes static data and dynamic data. The static data includes rated parameters, mechanical parameters, initial conditions, static load data, etc., specifically rated power, rated voltage, initial position, initial angle, initial speed, etc. when the motor starts, thrust or torque data when the motor is stationary or under static load, and power consumption under static load; dynamic data refers to data that changes with time during the operation of the motor, such as dynamic operating parameters, real-time load data, etc., specifically changes in current and voltage, speed and acceleration, thrust and torque, temperature, vibration, etc.

The acquisition device can be various sensors or a collection of various sensors. The sensor can be connected to the server by wire or wirelessly, and the real-time collected data is sent to the server for analysis and processing. The server refers to a hardware module with functions such as data reception, data processing, data storage, and data transmission, rather than the concept of a server in the prior art;

Since static data and dynamic data have different data characteristics, their processing methods and computational complexities are somewhat different. In this embodiment, it is also necessary to perform homomorphic processing on static data and dynamic data. In the prior art, homomorphic processing is a digital signal processing method. The process of transforming one type of input signal into another type of signal output using a homomorphic system is called homomorphic signal processing. In this embodiment, homomorphic processing refers to processing different types of (static and dynamic) collected data to form data that can be analyzed using the same method and has the same computational complexity, thereby avoiding differences in analysis results caused by using different methods to analyze different types. Specifically, performing homomorphic processing on static data and dynamic data further includes the following steps:

Step 110: preprocessing the static data and the dynamic data to extract static feature data and dynamic feature data;

Static data and dynamic data usually require different processing methods due to their different characteristics and application backgrounds. This processing method is not suitable for the application process of the push rod motor, so it needs to be preprocessed, where the preprocessing is divided into preprocessing of static data and preprocessing of dynamic data. Static data preprocessing can use statistical features, feature engineering and other methods to extract key static features, such as rated parameters and starting conditions of the motor, etc., and dynamic data preprocessing can use time series analysis, frequency domain analysis and other methods to extract dynamic features with time series, such as the spectrum distribution of the current waveform, the trend of speed change, etc., where statistical features, feature engineering, time series analysis, frequency domain analysis and other methods can refer to the existing technology and are not described in detail in this embodiment;

Step 111: establishing a static model based on known parameters and features and a dynamic model based on time series data respectively, and importing the extracted static feature data and dynamic feature data into the corresponding models;

After the static feature data and the dynamic feature data are extracted, they need to be processed by modeling. In the process of modeling, the correlation between different data is optimized. Therefore, in this embodiment, the integration processing is performed by establishing a static model and a dynamic model, wherein the static model is established based on known parameters and features, such as a linear regression model and a decision tree model; the dynamic model is established based on time series data, such as an autoregressive moving average model (ARMA), a seasonal decomposition model (Seasonal Decomposition of Time Series), etc. The above model has at least one data input terminal and a data output terminal, the data input terminal is used to input the static feature data and the dynamic feature data, and the data output terminal is used to output the result according to the input information;

Step 112: receiving output results of the static model and the dynamic model, wherein the static model outputs the operating state of the push rod motor, and the dynamic model outputs the change prediction of the push rod motor based on the time series;

After the static model and the dynamic model are established and the corresponding static feature data and dynamic feature data are inputted therein, the static model will output the operating state of the push rod motor, that is, the input static known parameter information is compared with the normal operating parameter information of the push rod motor, so as to judge its current operating state, and the operating state can be converted into a binary signal, for example, the output binary signal 1 is set to indicate that the push rod motor is operating normally, and the output binary signal 0 is set to indicate that the operation is abnormal, and the operating state of the push rod motor is judged according to the output result of the static model, wherein the conversion of the operating state into a binary signal can be realized by a logic circuit or a state machine;

Since the input of the dynamic model is dynamic feature data, and the dynamic model is established based on time series, the output of the dynamic model will include the change prediction of the push rod motor based on the time series, that is, the change prediction of the dynamic data based on the time series. After obtaining the change prediction result of the dynamic data based on the time series, the operating state of the push rod motor can be predicted, thereby obtaining the same type of output result as the static model (that is, the operating state of the push rod motor);

Step 113: judging the change prediction based on the time series output by the dynamic model through a threshold judgment method;

Before leaving the factory, the push rod motor will be tested and a dynamic parameter change value set with a time series will be generated. In existing application scenarios, this dynamic parameter change value set can only be used for factory inspection. After the push rod motor is installed, it can only be compared by disassembling the push rod motor and testing it again with the same test tool according to a fixed test procedure. In this embodiment, the change prediction based on the time series output by the dynamic model meets the factory test parameter type, so the dynamic parameter change value set can be called to judge the operation status of the push rod motor at each time point in the time series through the threshold judgment method;

The threshold determination method includes the following steps:

Step A: The server obtains a dynamic parameter change value set of the push rod motor factory inspection, and splits the dynamic parameter change value set;

The dynamic parameter change value set of the push rod motor factory inspection can be obtained by identifying the model and manufacturer of the push rod motor, or it can be manually input by the staff. After input, it is split according to the input parameter information to make each type of parameter independent, which is convenient for subsequent comparison;

Step B, comparing the push rod motor change prediction result based on the time series output by the dynamic model with the input dynamic parameter change value set to obtain a comparison result, and the comparison method can be difference comparison or ratio comparison;

Since there may be a zero value in the prediction result of the push rod motor change, such as a moving distance of zero, a current change of zero, etc., the comparison method in this embodiment can be a difference comparison or a ratio comparison method. When an error prompt appears during the ratio comparison process, it is adjusted to the difference comparison method;

Step C: setting the difference threshold and ratio threshold of the corresponding parameters, and judging the comparison result with the difference threshold or the ratio threshold, and outputting the operation status of the push rod motor with a time series.

After setting the difference threshold and ratio threshold of the corresponding parameters, the comparison result in step B is judged with the above threshold. When at least one dynamic parameter is greater than the set threshold, it is judged that the push rod motor is operating abnormally. When all dynamic parameters are less than the set threshold, it is judged that the push rod motor is operating normally. After the judgment is completed, the logic circuit or state machine is used to output the judgment result so that it is consistent with the output result type of the static model;

For example, taking the push rod linear stepper motor model NEMA23 as an example, the difference threshold or ratio threshold can be set according to the technical manual of the model. In this embodiment, it can also be set according to the operating error range of the push rod motor in the industry, so it is not described in detail.

Step 12: After the static data and dynamic data are processed in the same way, the abnormal data is eliminated and the actual operating state of the push rod motor is judged;

After completing the homomorphic processing of static data and dynamic data, interference may occur between the static and dynamic data. In subsequent analysis, the interference data needs to be eliminated. Specifically, the method for eliminating the interference data is to determine whether there is a correlation between the static data and the abnormal dynamic data in the processed data. If so, temporarily eliminate them and perform subsequent processing first. For example, if the temperature data of the static data and the instantaneous power data of the dynamic data are abnormal at the same time, it can be directly determined that the power of the push rod motor is abnormal, and the above data is temporarily eliminated, and the remaining data is processed to determine other operating fault information. The above process can be implemented using a correlation analysis method, such as the invention patent with publication number CN114531090A, which is not described in detail in this embodiment.

In the above process, the static model and the dynamic model respectively output the operating state of the push rod motor under static data and the operating state of the push rod motor under dynamic data. In actual applications, the static data and the dynamic data are generated at the same time, so they should also correspond to the same operating state. Therefore, it is also necessary to judge the actual operating state of the push rod motor according to the homomorphic processing result. Specifically, the method includes:

A time synchronization model is established, wherein the time synchronization model has two input terminals, wherein the two input terminals respectively input an output result of the static model and an output result of the dynamic model;

The time synchronization model can achieve time synchronization of different input sources so that they have the same timing information. Since static data generally does not have timing information, after the static data is input into the time synchronization model, the static data will be given timing information. The time synchronization model is provided with the Network Time Protocol (NTP). NTP is a widely used protocol that ensures that the device time is consistent with the global standard time by transmitting and synchronizing time information in a computer network. The NTP client connects to the NTP server and obtains time updates regularly, so that the device time is synchronized with the NTP server. After the output results of the static model and the output results of the dynamic model are input into the two input ends respectively, both will be output based on the synchronized time.

Synchronize and calibrate the time synchronization model so that the static model and the dynamic model can collect and output data based on the same timestamp;

In the process of using the time synchronization model, synchronization calibration is an essential step, because the output results of the dynamic model itself carry timing information, which may conflict with the set timestamp of the time synchronization model. Therefore, the time synchronization model needs to be synchronized and calibrated to make their timestamps the same;

Set a fixed calibration cycle and perform repeated calibration within the calibration cycle;

In order to realize automatic calibration, a fixed calibration period can also be set. During the fixed calibration period, the time synchronization model is synchronously calibrated. In this embodiment, the calibration period can be set to one hour. Alternatively, a calibration method based on the number of operations can be adopted. After the push rod motor runs a fixed number of times, a synchronous calibration is performed.

Monitoring the synchronization status of the time synchronization model during its operation;

During the operation of the time synchronization model, the synchronization status of the time synchronization model is monitored. When it is found that the timing information of the output result of the dynamic data conflicts with the timestamp set by the time synchronization model, a calibration is automatically triggered.

After time synchronization, the operating state in which the static model output result is the same as the dynamic model output result at the same timestamp is used as the operating state of the push rod motor.

For example, after time synchronization, at the same timestamp, if the output result of the static model is binary 1 and the output result of the dynamic model is also binary 1, it can be judged that the push rod motor is operating normally. At the same timestamp, if the output result of any model is binary 0, it is judged that the push rod motor is operating abnormally.

Step 13: Convert the operation status judgment result of the push rod motor and send it to the user end to complete the detection of the push rod motor.

After obtaining the judgment result, it needs to be converted and sent to the user end, for example, the judgment information is converted into an electrical signal and sent to the display device. The display device will display "device abnormality" or "device normal" and attach the timestamp information of the current time synchronization model to facilitate subsequent maintenance.

Embodiment 2: As shown in FIG2 , this embodiment proposes a push rod motor performance detection system for old data analysis, which runs the detection method in Embodiment 1. The system includes the following modules:

The data receiving and homomorphic processing module is used for the server to receive static data and dynamic data during the operation of the push rod motor. The static data and dynamic data are collected by the collection device, and the static data and dynamic data are processed homomorphically after receiving.

The actual operation status judgment module is used to remove abnormal data after isomorphic processing of static data and dynamic data, and to judge the actual operation status of the push rod motor;

The detection result display module is used to convert the operation status judgment result of the push rod motor and send it to the user end to complete the detection of the push rod motor.

The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (8)

1. A push rod motor performance detection method based on data analysis is characterized in that: the detection method comprises the following steps:

the server receives static data and dynamic data in the running process of the push rod motor, the static data and the dynamic data are collected by the collecting equipment, and homomorphic processing is carried out on the static data and the dynamic data after the receiving is completed;

after homomorphic processing is carried out on static data and dynamic data, abnormal data are removed, and the actual running state of the push rod motor is judged;

Converting and transmitting the running state judgment result of the push rod motor to a user side to finish the detection of the push rod motor;

Homomorphic processing of static and dynamic data further comprises the steps of:

Preprocessing static data and dynamic data, and extracting static characteristic data and dynamic characteristic data;

respectively establishing a static model based on known parameters and features and a dynamic model based on time sequence data, and importing the extracted static feature data and dynamic feature data into corresponding models;

Receiving output results of a static model and a dynamic model, wherein the static model outputs the running state of a push rod motor, and the dynamic model outputs the change prediction of the push rod motor based on a time sequence;

judging the change prediction based on the time sequence output by the dynamic model through a threshold judgment method;

The threshold judgment method comprises the following steps:

the server acquires a dynamic parameter change value set of the push rod motor in factory detection, and splits the dynamic parameter change value set;

Comparing the change prediction result of the push rod motor output by the dynamic model with the input dynamic parameter change value set based on the time sequence to obtain a comparison result, wherein the comparison mode is difference comparison or ratio comparison, and when an error prompt occurs in the ratio comparison process, the comparison mode is adjusted to be difference comparison mode;

Setting a difference threshold and a ratio threshold of corresponding parameters, judging the comparison result and the difference threshold or the ratio threshold, and outputting the running state of the push rod motor with a time sequence.

2. The method for detecting the performance of the push rod motor based on data analysis according to claim 1, wherein the method comprises the following steps: the static model is built based on known parameters and features, the dynamic model is built based on time series data, the static model and the dynamic model are provided with at least one data input end and a data output end, the data input end is used for inputting static feature data and dynamic feature data, and the data output end is used for outputting results according to input information.

3. The method for detecting the performance of the push rod motor based on data analysis according to claim 1, wherein the method comprises the following steps: the judging of the actual running state of the push rod motor comprises the following steps:

Establishing a time synchronization model, wherein the time synchronization model is provided with two input ends, and the two input ends respectively input an output result of a static model and an output result of a dynamic model;

performing synchronous calibration on the time synchronous model to enable the static model and the dynamic model to perform data acquisition and data output based on the same time stamp;

setting a fixed calibration period, and carrying out repeated calibration in the calibration period;

monitoring the synchronous state of the time synchronization model in the running process of the time synchronization model;

And after time synchronization, taking the running state of which the static model output result is the same as the dynamic model output result as the running state of the push rod motor under the same time stamp.

4. The method for detecting the performance of the push rod motor based on data analysis according to claim 1, wherein the method comprises the following steps: the homomorphism processing refers to processing collected data of different types to form data which can adopt the same method and have the same calculation complexity.

5. The method for detecting the performance of the push rod motor based on data analysis according to claim 1, wherein the method comprises the following steps: the static data is preprocessed, the key static features are extracted by adopting a statistical feature or feature engineering method, and the dynamic data is preprocessed, and the dynamic features with time sequences are extracted by adopting a time sequence analysis or frequency domain analysis method.

6. The method for detecting the performance of the push rod motor based on data analysis according to claim 1, wherein the method comprises the following steps: the output ends of the static model and the dynamic model are provided with logic circuits or state machines, and data results are output in the form of binary signals.

7. The method for detecting the performance of the push rod motor based on data analysis according to claim 6, wherein the method comprises the following steps: a binary signal 1 indicates that the push rod motor is operating normally, and a binary signal 0 indicates that the push rod motor is operating abnormally.

8. The utility model provides a push rod motor performance detecting system based on data analysis which characterized in that: a push rod motor performance detection method for implementing the data analysis-based push rod motor according to any one of claims 1 to 7, the system comprising:

the data receiving and homomorphic processing module is used for receiving static data and dynamic data in the running process of the push rod motor by the server, acquiring the static data and the dynamic data by the acquisition equipment, and homomorphic processing is carried out on the static data and the dynamic data after the receiving is completed;

the actual running state judging module is used for eliminating abnormal data after homomorphic processing is carried out on the static data and the dynamic data, and judging the actual running state of the push rod motor;

the detection result display module is used for converting the running state judgment result of the push rod motor and sending the result to the user side to finish the detection of the push rod motor;

Homomorphic processing of static and dynamic data further comprises the steps of:

Preprocessing static data and dynamic data, and extracting static characteristic data and dynamic characteristic data;

respectively establishing a static model based on known parameters and features and a dynamic model based on time sequence data, and importing the extracted static feature data and dynamic feature data into corresponding models;

Receiving output results of a static model and a dynamic model, wherein the static model outputs the running state of a push rod motor, and the dynamic model outputs the change prediction of the push rod motor based on a time sequence;

judging the change prediction based on the time sequence output by the dynamic model through a threshold judgment method;

The threshold judgment method comprises the following steps:

the server acquires a dynamic parameter change value set of the push rod motor in factory detection, and splits the dynamic parameter change value set;

Comparing the change prediction result of the push rod motor output by the dynamic model with the input dynamic parameter change value set based on the time sequence to obtain a comparison result, wherein the comparison mode is difference comparison or ratio comparison, and when an error prompt occurs in the ratio comparison process, the comparison mode is adjusted to be difference comparison mode;

Setting a difference threshold and a ratio threshold of corresponding parameters, judging the comparison result and the difference threshold or the ratio threshold, and outputting the running state of the push rod motor with a time sequence.