CN-121995215-A - High-frequency electric signal characteristic extraction method and system for EMB system

Abstract

The application relates to the technical field of feature extraction, in particular to a high-frequency electric signal feature extraction method and a system for an EMB system, wherein the method comprises the steps of collecting current data of each phase of a three-phase permanent magnet synchronous motor of the EMB system in a first period in real time, and injecting a high-frequency current signal into a stator winding of the three-phase permanent magnet synchronous motor in a second period, and then obtaining a zero-sequence voltage signal of the three-phase permanent magnet synchronous motor; dividing the first period into each period, obtaining the waveform unbalance degree of each period to obtain the characteristic quantity of the inter-turn short circuit fault diagnosis, obtaining the peak-valley difference coefficient of each period to obtain the characteristic quantity of the local demagnetizing fault diagnosis, and respectively obtaining the consistency factors of the inter-turn short circuit fault diagnosis and the local demagnetizing fault diagnosis and the comprehensive diagnosis characteristic value. The application aims to ensure that the characteristic information related to faults is effectively extracted under various working conditions by a fusion phase current analysis method and a zero sequence voltage response analysis method based on high-frequency current signal injection.

Inventors

• NI CHUNYANG
• ZHANG TAO

Assignees

• 湖北域控智驱科技有限公司

Dates

Publication Date

20260508

Application Date

20260409

Claims (10)

1. 1. A method for extracting characteristics of high-frequency electrical signals for an EMB system, the method comprising the steps of: Collecting current data of each phase of a three-phase permanent magnet synchronous motor of an EMB system in a preset first period in real time, and injecting high-frequency current signals into a stator winding of the three-phase permanent magnet synchronous motor in a preset second period, wherein the zero-sequence voltage signals of the three-phase permanent magnet synchronous motor are obtained; Dividing the first period into periods, and obtaining waveform unbalance of each period by comparing the distribution difference of current data of different phases in each period so as to obtain characteristic quantity of turn-to-turn short circuit fault diagnosis; Measuring peaks Gu Chayi of current data of each phase in each period, and obtaining peak-to-valley difference coefficients of each period according to the similarity degree of the peak-to-valley differences of different phases in each period and the discrete degree of the peak-to-valley differences of each phase in each period, so as to obtain the characteristic quantity of the diagnosis of the local demagnetizing faults; Obtaining consistency factors of turn-to-turn short circuit fault diagnosis and local demagnetizing fault diagnosis through waveform unbalance degree and discrete condition of peak Gu Chayi coefficient of all periods respectively; And counting the injection frequency of the high-frequency current signal and the offset frequency when the local demagnetizing fault occurs under the current working condition, and respectively acquiring comprehensive diagnosis characteristic values of inter-turn short circuit fault diagnosis and local demagnetizing fault diagnosis by combining the amplitude value in the spectrogram of the zero-sequence voltage signal, the characteristic values and consistency factors of inter-turn short circuit fault diagnosis and local demagnetizing fault diagnosis.
2. 2. The method for extracting characteristics of high-frequency electrical signals for an EMB system according to claim 1, wherein the waveform unbalance obtaining process is as follows: The method comprises the steps of obtaining each peak point and each trough point of current data of each phase in each period in time sequence, calculating the difference value between each peak point and each adjacent trough point, taking the average value of all the corresponding difference values in each period as the average peak-to-trough distance of each phase in each period; the average peak-to-valley distances of all phases in each period are arranged in descending order, and the expression of the waveform unbalance degree of each period is as follows: In the formula (I), in the formula (II), A waveform unbalance degree indicating an i-th period; representing the maximum of the average peak-to-valley distances of all phases in the ith period; representing the median value in the average peak-to-valley distances of all phases in the ith period; Representing the minimum of the average peak-to-valley distances of all phases in the ith period; representing a preset positive number.
3. 3. The high-frequency electrical signal feature extraction method for EMB system according to claim 1, wherein the feature quantity of the turn-to-turn short circuit fault diagnosis is an average value of waveform unbalance degrees of all periods.
4. 4. The method for extracting the characteristics of the high-frequency electrical signal for the EMB system according to claim 2, wherein the process of obtaining the peak Gu Chayi coefficients is as follows: acquiring the out-of-phase waveform similarity of each period through the similarity degree of the difference values among different phases in each period; Acquiring the in-phase waveform fluctuation degree of each period through the discrete degree of each corresponding difference value in each period; mapping the out-of-phase waveform similarity and the in-phase waveform fluctuation to a first positive number and a second positive number respectively; the peak Gu Chayi coefficient is the product of the first positive number and the second positive number.
5. 5. The method for extracting characteristics of high-frequency electrical signals for an EMB system according to claim 4, wherein the step of obtaining the out-of-phase waveform similarity is: And calculating the similarity of the difference values between any two phases in each period, wherein the out-of-phase waveform similarity is the average value of the similarity between all the two phases in each period.
6. 6. The method for extracting the characteristics of the high-frequency electric signal for the EMB system according to claim 4, wherein the in-phase waveform fluctuation degree is obtained by the following steps: the dispersion degree of all the difference values of each phase in each period is recorded as the peak-to-valley distance dispersion degree of each phase in each period; The in-phase waveform fluctuation degree is the average value of the peak-to-valley distance dispersion degree of all phases in each period.
7. 7. The high-frequency electric signal feature extraction method for EMB system according to claim 1, wherein the feature quantity of the local demagnetization fault diagnosis is an average value of peak-to-valley difference coefficients of all periods.
8. 8. The method for extracting the characteristics of the high-frequency electrical signal for the EMB system according to claim 1, wherein the method for obtaining the consistency factor of the turn-to-turn short circuit fault diagnosis and the local demagnetizing fault diagnosis is as follows: the consistency factor of the turn-to-turn short circuit fault diagnosis is the reciprocal of the sum of the dispersion of the waveform unbalance of all periods and a constant which is preset to be more than 0; the consistency factor of the local demagnetization fault diagnosis is the inverse of the sum of the dispersion of the peak-valley difference coefficient of all periods and a constant which is preset to be larger than 0.
9. 9. The high-frequency electrical signal characteristic extraction method for EMB system according to claim 1, wherein the expressions of the comprehensive diagnostic characteristic values of the turn-to-turn short circuit fault diagnosis and the local demagnetizing fault diagnosis are respectively: In the formula (I), in the formula (II), Comprehensive diagnosis characteristic values for diagnosing turn-to-turn short circuit faults are represented; normalized values representing feature quantities for turn-to-turn short circuit fault diagnosis; a normalized value representing a consistency factor for turn-to-turn short fault diagnosis; A normalized value representing an amplitude value at an injection frequency in a spectrogram of the zero sequence voltage signal; In the formula (I), in the formula (II), Comprehensive diagnosis characteristic values for representing local demagnetization fault diagnosis; Normalized values representing feature quantities for local demagnetization fault diagnosis; Normalized values of the consistency factor representing the local demagnetizing fault diagnosis; A normalized value representing the amplitude value at the offset frequency in the spectrogram of the zero sequence voltage signal.
10. 10. A high frequency electrical signal feature extraction system for an EMB system comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor implements the steps of a high frequency electrical signal feature extraction method for an EMB system according to any one of claims 1-9 when the computer program is executed by the processor.

Description

High-frequency electric signal characteristic extraction method and system for EMB system Technical Field The application relates to the technical field of feature extraction, in particular to a high-frequency electric signal feature extraction method and system for an EMB system. Background An electro-mechanical brake (EMB) system is used as a fully-wire-controlled intelligent brake system, and a three-phase permanent magnet synchronous motor is controlled by an electric signal to generate braking force, so that hydraulic oil, compressed air and complex pipelines in a traditional brake system are replaced, and the intelligent brake system is a key technology for realizing high-order automatic driving and an intelligent chassis. The three-phase permanent magnet synchronous motor (PERMANENT MAGNET Synchronous Motor, PMSM) is used as a core driving element of the EMB system, and the health state directly determines the driving safety. The three-phase permanent magnet synchronous motor generally consists of a stator and a rotor, wherein the turn-to-turn short circuit fault of a stator winding and the local demagnetization fault of the rotor are common stator faults and rotor faults respectively, and both the stator faults and the rotor faults can cause sudden increase of motor phase current and unbalance of three-phase current, so that the local temperature in the motor is overhigh, the output torque is reduced, the EMB system is possibly invalid, and serious safety accidents are caused. The existing three-phase permanent magnet synchronous motor of the EMB system depends on the working condition of the motor, under the conditions of low speed and light load, high-frequency electric signals caused by faults are very weak and are easily covered by factors such as sensor noise, measurement errors, nonlinearity of an inverter and the like, and the characteristics of fault electric signals of turn-to-turn short circuit faults of a stator winding and local demagnetizing faults of a rotor are similar, so that the faults of the three-phase permanent magnet synchronous motor of the EMB system are easily misdiagnosed. Disclosure of Invention In view of the foregoing, it is necessary to provide a high-frequency electrical signal feature extraction method and system for an EMB system, which can effectively extract feature information related to faults under various working conditions by a fused phase current analysis method and a zero sequence voltage response analysis method based on high-frequency current signal injection, compared with the conventional high-frequency electrical signal feature extraction method for an EMB system: in a first aspect, an embodiment of the present application provides a method for extracting a high-frequency electrical signal feature of an EMB system, including the steps of: Collecting current data of each phase of a three-phase permanent magnet synchronous motor of an EMB system in a preset first period in real time, and injecting high-frequency current signals into a stator winding of the three-phase permanent magnet synchronous motor in a preset second period, wherein the zero-sequence voltage signals of the three-phase permanent magnet synchronous motor are obtained; Dividing the first period into periods, and obtaining waveform unbalance of each period by comparing the distribution difference of current data of different phases in each period so as to obtain characteristic quantity of turn-to-turn short circuit fault diagnosis; Measuring peaks Gu Chayi of current data of each phase in each period, and obtaining peak-to-valley difference coefficients of each period according to the similarity degree of the peak-to-valley differences of different phases in each period and the discrete degree of the peak-to-valley differences of each phase in each period, so as to obtain the characteristic quantity of the diagnosis of the local demagnetizing faults; Obtaining consistency factors of turn-to-turn short circuit fault diagnosis and local demagnetizing fault diagnosis through waveform unbalance degree and discrete condition of peak Gu Chayi coefficient of all periods respectively; And counting the injection frequency of the high-frequency current signal and the offset frequency when the local demagnetizing fault occurs under the current working condition, and respectively acquiring comprehensive diagnosis characteristic values of inter-turn short circuit fault diagnosis and local demagnetizing fault diagnosis by combining the amplitude value in the spectrogram of the zero-sequence voltage signal, the characteristic values and consistency factors of inter-turn short circuit fault diagnosis and local demagnetizing fault diagnosis. In one embodiment, the waveform unbalance obtaining process is as follows: The method comprises the steps of obtaining each peak point and each trough point of current data of each phase in each period in time sequence, calculating the difference value between each pe