Method for judging health degree of motor by comparing real-time acquired data with characteristic values

Abstract

According to the invention, motor operation data in a motor protector, a frequency converter for driving the motor to operate and a soft starter are collected online in real time, and the collected data are stored in a data server through a high-speed industrial Ethernet in an enterprise; and the motor operation data stored in the data server is compared with the motor electrical data characteristic value stored in the data server, so that the health degree of the motor is judged, and the current health degree of the motor is mastered in real time.

Description

Technical Field

[0001] A method for detecting the health of a motor, in particular a method for obtaining the real-time health of a motor by comparing the real-time running data of the motor collected online with pre-stored characteristic values. Background Art

[0002] Electric motors play an important role in enterprises. Some large enterprises usually need to equip thousands or even tens of thousands of different types of electric motors to achieve large-scale production. Electric motors are widely used in drive equipment in enterprises to improve production efficiency. The performance and quality of electric motors directly affect the production efficiency and product quality of enterprises. Therefore, enterprises need to strictly manage and maintain electric motors to ensure their normal operation. The electrical equipment used for motor protection generally includes motor protectors, frequency converters and soft starters. These electrical equipment generally have data interfaces, but are mainly used for centralized control rather than data collection. The inspection of the status of the motor itself still relies on skilled workers' inspections. It can only rely on experience to determine how long the motor can run and to perform maintenance according to plan. As a result, the utilization rate of the motor is reduced and the inventory of the motor increases. If the motor is out of stock, it may affect the continued production. Summary of the invention

[0003] In order to solve the above problems, the present invention collects the motor operation data of the motor protector, the frequency converter and the soft starter that drive the motor in real time online, stores the collected data to the data server through the high-speed industrial Ethernet within the enterprise, and compares the motor operation data stored in the data server with the motor electrical data characteristic values ​​stored in the data server to judge the health of the motor, so as to grasp the current health of the motor in real time.

[0004] The technical solution of the present invention is to establish a high-speed industrial Ethernet and a real-time motor operation data acquisition system within the enterprise, which is used to store the real-time motor operation data of the motor protector, the frequency converter and the soft starter that drive the motor operation in real time into a data server, sample and analyze the real-time motor operation data and motor performance indicators, determine the characteristic values ​​of motor health assessment, fault diagnosis and fault prediction, compare the real-time motor operation data with the characteristic values ​​of the motor, early discover and predict the faults and hidden dangers of the motor, evaluate the inherent long-term health characteristics and short-term real-time operation health status of the motor based on sampling data analysis and modeling methods, predict motor failures in advance, and provide a scientific basis for motor maintenance and the formulation of motor inventory strategies.

[0005] The characteristic values ​​of the motor are selected from the electrical parameter values ​​that can reflect the operating status of the motor. These characteristic values ​​may include three-phase voltage (Uab, Ubc, Uca), three-phase current (Ia, Ib, Ic), rated current, starting current, short-circuit current, rated power, active power, reactive power, apparent power, power factor, frequency, insulation resistance, ground resistance, torque, speed, vibration and three-phase stator winding temperature.

[0006] When the motor operates normally, the electrical parameter values ​​of the motor are the characteristic values ​​of the motor.

[0007] The selection of eigenvalues ​​does not select all eigenvalues ​​of the motor, but only selects eigenvalues ​​with relatively large offsets of the electrical parameter values ​​of the motor when the motor is in an abnormal condition or even fails, and selects eigenvalues ​​with relatively fast response speeds.

[0008] In order to accurately judge the health of a motor, it is usually necessary to comprehensively consider the changes in multiple characteristic values ​​and analyze them in combination with the motor's historical operating data and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 Flowchart for motor health analysis calculation. DETAILED DESCRIPTION

[0010] The health calculation includes: sampling, reading and saving of key data; generation of characteristic values; fault diagnosis and prediction; analysis and calculation of external interference factors; and analysis of equipment operation history data.

[0011] Sampling of key data:

[0012] 1. The motor operates under five operating conditions: no fault condition, phase failure condition, overload fault condition, motor leakage fault condition, and motor bearing damage;

[0013] 2. Three driving modes: direct starting with a motor protector, soft starting with a soft starter, and variable frequency starting with a frequency converter.

[0014] 3. The three-phase power supply voltage of the motor is 80% voltage, 85% voltage, 90% voltage, 95% voltage, 100% voltage, 105% voltage, 110% voltage, 115% voltage and 120% voltage respectively;

[0015] 4. The motor loads are no load, 30% load, 50% load, 70% load and 100% load respectively;

[0016] 5. When the motor is driven by a frequency converter, the frequencies of the frequency converter are 30 Hz, 35 Hz, 40 Hz, 45 Hz, and 50 Hz respectively;

[0017] 6. Under the above combination, various electrical real-time operation data of the motor are collected, including three-phase voltage (Uab, Ubc, Uca), three-phase current (Ia, Ib, Ic), rated current, starting current, short-circuit current, rated power, active power, reactive power, apparent power, power factor, frequency, insulation resistance, grounding resistance, torque, speed, vibration and three-phase stator winding temperature. The sampling frequency is to record the electrical operation data of the motor once per second and save it in the data server;

[0018] 7. Collection of real-time operation data of the motor: Install temperature sensors in the windings of the corresponding motors to obtain real-time temperature data of the three-phase windings of the motors during operation;

[0019] 8. Obtain the real-time temperature and vibration data of the motor housing from the temperature and vibration sensor fixed on the corresponding motor housing;

[0020] 9. Obtain real-time electrical data of the motor during operation from the multi-function meter installed in the corresponding motor distribution cabinet;

[0021] 10. Get real-time electrical data from soft starters and inverters while the motor is running.

[0022] The generation of characteristic values ​​is to pre-process the collected raw data to remove noise, outliers and interference signals. Through time domain analysis, frequency domain analysis, statistical analysis, and calculation and analysis based on the mathematical model and physical model of the motor, the calculated results are compared with the electrical operation data of the motor under normal conditions to obtain the correlation coefficient of the electrical operation data of the motor in each state. The characteristic values ​​are formed with the values ​​with larger numerical changes and faster reactions, and the fault codes are compiled according to the collected data and stored in the data server.

[0023] Health assessment, taking into account environmental interference factors, equipment operation history and fault maintenance history, the motor equipment full operating health status assessment process is as follows: Figure 1 As shown in Figure 1, the health assessment calculation process mainly includes three parts.

[0024] The first part is real-time fault diagnosis and prediction analysis. First, the basic information and acquisition parameters of the motor are obtained, and the sampling parameters at each sampling moment are classified, counted and recorded. The fault diagnosis and prediction of the real-time operating point data is to preliminarily determine the severity of the fault by reading the fault code. For emergency faults, an alarm prompt is issued quickly. At the same time, the fault data is further classified into fault types, the deviation between the real-time operating condition characteristic value and the fault data is calculated, and the real-time health assessment impairment value is given. Finally, the alarm information and maintenance suggestions are given according to the fault type, and the fault operation time is recorded.

[0025] The second part is the analysis of the impact of environmental interference factors. First, the real-time collected environmental data is compared with the standard temperature characteristic value of the normal operation of the equipment, and the temperature deviation value is calculated by considering the environmental interference temperature deviation coefficient. The same processing method is also adopted for other environmental interference parameters, such as humidity and dust particle analysis.

[0026] The third part is the analysis of the operation, failure and maintenance history of the motor. First, the equipment working conditions and failure maintenance history statistics of the health assessment interception time period are classified and calculated. The motor working conditions are classified into shutdown state, light load state, medium load state, heavy load state, full load state and overload state. The weight distribution coefficient and conversion value of the impact of different working conditions on the motor are calculated according to the load proportion. For the failure maintenance history statistics, such as the number of overhauls and minor repairs of the motor, and the replacement history of core parts or small parts, they are classified and calculated with different weighting coefficients. Secondly, the running time of the motor from commissioning to the evaluation time is calculated, and the health depreciation or appreciation of the motor's failure maintenance history is comprehensively calculated. Finally, the health assessment values ​​of the above three parts are statistically calculated according to the weight coefficient distribution value to obtain the health score of the motor, and displayed as a percentage, and the corresponding motor maintenance strategy is given.

[0027] The above descriptions are merely embodiments of the present invention and do not limit the present invention in any form. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A method for judging the health of a motor by comparing real-time collected data with characteristic values, characterized in that: Establish a high-speed industrial Ethernet and real-time motor operation data acquisition system within the enterprise to store the real-time motor operation data collected in the motor protector, the inverter and soft starter that drive the motor to run in the data server, sample and analyze the real-time motor operation data and motor performance indicators, determine the characteristic values ​​of motor health assessment, fault diagnosis and fault prediction, compare the real-time motor operation data with the characteristic values ​​of the motor, and early detect and predict the faults and hidden dangers of the motor. Based on the sampling data analysis and modeling method, the inherent long-term health characteristics and short-term real-time operation health status of the motor are evaluated, and the motor fault is predicted in advance, providing a scientific basis for the formulation of motor maintenance and motor inventory strategies. The characteristic values ​​of the motor are selected from the electrical parameter values ​​that can reflect the operating status of the motor. These characteristic values ​​can include three-phase voltage (Uab, Ubc, Uca), three-phase current (Ia, Ib, Ic), rated current, starting current, short-circuit current, rated power, active power, reactive power, apparent power, power factor, frequency, insulation resistance, grounding resistance, torque, speed, vibration and three-phase stator winding temperature. When the motor is running normally, the electrical parameter value of the motor is the characteristic value of the motor. The selection of characteristic values ​​will not select all the characteristic values ​​of the motor. Only the characteristic values ​​with larger offset of the electrical parameter value of the motor when the motor is in abnormal condition or even fails, and the characteristic values ​​with faster response speed are selected. In order to accurately judge the health of the motor, it is usually necessary to comprehensively consider the changes of multiple characteristic values ​​and analyze them in combination with the historical operation data and experience of the motor. The collected raw data is preprocessed to remove noise, abnormal values ​​and interference signals. Through time domain analysis, frequency domain analysis, statistical analysis, and calculation and analysis based on the mathematical model and physical model of the motor, the calculated results are compared with the electrical operation data of the motor under normal state, and the correlation coefficient of the electrical operation data of the motor between each state is obtained. The characteristic value is formed by the value with larger value change and faster response, and the fault code is compiled according to the collected data and stored in the data server. The health assessment calculation process mainly includes three parts. The first part is real-time fault diagnosis and prediction analysis. First, the basic information and acquisition parameters of the motor are obtained and evaluated. The sampling parameters at each sampling moment are classified, counted and recorded. The fault diagnosis and prediction of the real-time operating point data is to preliminarily determine the severity of the fault by reading the fault code. For emergency faults, an alarm prompt is issued quickly. At the same time, the fault data is further classified into the fault type, the deviation between the real-time operating condition characteristic value and the fault data is calculated, and the real-time health assessment impairment value is given. Finally, the alarm information and maintenance opinions are given according to the fault type, and the fault operation time is recorded. The second part is the impact analysis of environmental interference factors. First, the real-time collected environmental data is compared with the standard temperature characteristic value of the normal operation of the equipment, and the temperature deviation value is calculated considering the environmental interference temperature deviation coefficient.The same treatment method is also adopted for other parameters of environmental interference, such as humidity and dust particle analysis. The third part is the analysis of motor operation, failure and maintenance history. First, the equipment working condition classification and failure maintenance history statistics of the health assessment interception time period are calculated. For the classification statistics of motor working conditions, they are divided into shutdown state, light load state, medium load state, heavy load state, full load state and overload state. The weight distribution coefficient and conversion value of the impact of different working conditions on the motor are calculated according to the load proportion. For the failure maintenance history statistics, such as the number of overhauls and minor repairs of the motor, and the replacement history of core accessories or small accessories, they are classified and calculated with different weighting coefficients. Secondly, the running time of the motor from commissioning to the evaluation time is calculated, and the health depreciation or appreciation of the motor's failure maintenance history is comprehensively calculated. Finally, the health assessment values ​​of the above three parts are statistically calculated according to the weight coefficient distribution value to obtain the health score of the motor, and displayed as a percentage, and the corresponding motor maintenance strategy is given at the same time.

Images