CN112393906B

CN112393906B - Method for diagnosing, classifying and evaluating health of weak signal fault of bogie bearing of metro vehicle

Info

Publication number: CN112393906B
Application number: CN202011174434.5A
Authority: CN
Inventors: 戎芳明; 贾小平; 杨陈; 王兴元; 徐步震; 金鑫
Original assignee: CRRC Nanjing Puzhen Co Ltd
Current assignee: CRRC Nanjing Puzhen Co Ltd
Priority date: 2020-10-28
Filing date: 2020-10-28
Publication date: 2023-04-07
Anticipated expiration: 2040-10-28
Also published as: CN112393906A

Abstract

The invention relates to a method for diagnosing, classifying and evaluating the health of weak signal faults of a metro vehicle bogie bearing, which comprises the steps of collecting bearing acceleration signals in a metro running state, and acquiring normal vibration data under different working conditions and vibration data under different fault working conditions; extracting the characteristics of the obtained state signals, and decomposing the state signals by an improved ensemble empirical mode decomposition method to obtain a plurality of intrinsic mode components; selecting a key eigenmode component signal to reconstruct an original signal by combining the energy of the eigenmode component through a fast spectral kurtosis diagram and a correlation coefficient to obtain a new eigenmode component characteristic set; identifying early weak fault signals of the bearing; processing an early weak fault signal, and judging whether a bearing is invalid or not and judging the current health grade of the bearing; the invention can adopt different active maintenance means and strategies according to different health grades, thereby effectively prolonging the service life of the rolling bearing and avoiding unnecessary loss.

Description

Method for diagnosing, classifying and evaluating health of weak signal fault of bogie bearing of metro vehicle

Technical Field

The invention relates to a method for diagnosing, classifying and evaluating health of weak signal faults of a metro vehicle bogie bearing, and belongs to the field of rail transit.

Background

According to the composition structure of the rolling bearing, the faults can be divided into inner ring faults, outer ring faults, rolling body faults and retainer faults, or composite faults of two or more faults. In the prior art, the detection of the faults of the rolling bearing of the metro vehicle bogie has the problems that early faults cannot be diagnosed, the faults cannot be classified and the fault diagnosis is not accurate enough, and the like.

At present, the maintenance aiming at the bearing of the bogie mainly comprises two ways: the first is traditional manual detection, comes the dismouting to survey the bearing state through whether there is abnormal sound, unusual vibration, and the second is through gathering the vibration data of journal box bearing, and the contrast has bearing fault characteristic frequency signal. The former depends on subjective judgment and is time-consuming and labor-consuming, and the latter generally exists in the terminal stage of bearing failure when abnormal warning is detected and has a high probability of wrong diagnosis. In the traditional frequency domain analysis method, a person mainly diagnoses the fault by observing whether a spectrum peak exists at the fault characteristic frequency in a spectrogram, and the diagnosis efficiency and the diagnosis precision are low.

Disclosure of Invention

The invention provides a method for diagnosing, classifying and evaluating the weak signal fault of a metro vehicle bogie bearing, which can adopt different active maintenance means and strategies according to different health grades, thereby effectively prolonging the service life of a rolling bearing and avoiding unnecessary loss.

The technical scheme adopted by the invention for solving the technical problem is as follows:

a method for diagnosing, classifying and evaluating health of weak signal faults of a metro vehicle bogie bearing comprises the following steps:

the method comprises the steps of firstly, acquiring a bearing acceleration signal in a subway running state, and acquiring normal vibration data under different working conditions and vibration data under different fault working conditions;

secondly, extracting the characteristics of the obtained state signals, and decomposing the state signals by an improved ensemble empirical mode decomposition method to obtain a plurality of intrinsic mode components;

thirdly, selecting key eigenmode component signals to reconstruct original signals by combining the energy of a plurality of obtained eigenmode components through a fast spectral kurtosis diagram and a correlation coefficient to obtain a new eigenmode component feature set;

fourthly, diagnosing based on the reconstructed signal, and identifying an early weak fault signal of the bearing;

fifthly, processing the identified early weak fault signal, judging whether the bearing fails or not, and judging the health grade of the current bearing;

as a further preferable aspect of the present invention, the fourth step of performing diagnosis based on the reconstructed signal specifically includes the steps of:

step 41, determining band-pass parameters of the reconstructed signal through a fast spectral kurtosis map;

step 42, performing improved resonance demodulation processing after the determined band-pass parameter filtering processing, performing bearing fault diagnosis through a square envelope spectrum, and judging whether the bearing is failed;

as a further preferable aspect of the present invention, in the fifth step, the judging of the current health level of the bearing specifically includes the following steps:

51, carrying out neural network training on the new eigenmode component feature set obtained in the third step to form a bearing fault model;

step 52, utilizing the trained bearing fault model to identify the fault type;

step 53, forming the characteristic feature set of the eigenmode component into a reference space (MS) _N N = 10), indicating that each IMF in the reference space may have 10 degrees of freedom, i.e. the effective characteristic parameter is: the method comprises the steps of averaging, peak-to-peak values, effective values, peak factors, kurtosis, multi-point kurtosis, skewness, shape coefficients, pulse coefficients and margin factors, and then calculating the Mahalanobis distances of various existing bearings;

step 54, constructing a bearing health index and health grade table of [0, 100] according to the existing Mahalanobis distance;

as a further preference of the present invention, the health index of the bearing is set between 0 and 100, the larger the value, the healthier the bearing state is; an index of 100 indicates that the target bearing is in an optimal state, an index of 0 indicates that the target bearing is completely failed, and the health state is graded to be good (85, 100), healthy (70, 85), sub-healthy (55, 70), early warning (40, 55), warning (25, 40), failure [0, 25];

as a further preferred embodiment of the invention, a three-way acceleration sensor is installed at the top end of the bearing to be tested, a three-way acceleration sensor is installed at the bottom end of the bearing to be tested, two three-way acceleration sensors are powered by a power supply, and a data acquisition card acquires vibration acceleration values of the bearing to be tested in three directions and transmits the vibration acceleration values to a computer for analysis to acquire data required in the first step;

as a further preferred embodiment of the present invention, the method includes two modes, the first mode is a learning mode, and after the fifth step is completed, the identified early weak fault signal is processed to determine whether the bearing is failed, and determine the current health level of the bearing, and these data are periodically stored;

the second is an alarm mode, and after the fifth step is completed, the identified early weak fault signal is processed to judge whether the bearing fails or not, judge the health grade of the current bearing and only store the fault data in the bearing;

as a further preferable aspect of the present invention, the algorithm for neural network training in diagnosing or discriminating the current health level of the bearing based on the reconstructed signal employs a genetic algorithm.

Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:

1. according to the diagnosis, classification and health assessment method based on the bogie bearing fault diagnosis and health assessment method, simulation, test and engineering practice show that weak faults of the bogie bearing can be effectively identified;

2. the invention can detect the fault of the weak signal of the bearing in the early stage and can accurately identify the fault, thereby forming engineering benefit;

3. the method can quickly position the type of the bearing fault, thereby analyzing the path causing the fault and reducing the occurrence rate of the fault on the path;

4. the invention can make a bearing maintenance strategy and a bearing operation replacement strategy through the health evaluation grade, thereby forming the benefits of reducing the replacement cost and saving the general investigation time.

Drawings

The invention is further illustrated with reference to the following figures and examples.

FIG. 1 is a flow chart of bearing fault diagnosis provided by the present invention;

FIG. 2 is a schematic diagram of a bearing weak fault signal extraction process provided by the present invention;

FIG. 3 is a flow chart of a genetic algorithm used for training and diagnosing faults of a sample according to the present invention;

FIG. 4 is a health status level table provided by the present invention;

FIG. 5 is a flow chart of a bearing state of health ranking technique provided by the present invention;

FIG. 6 is a schematic flow chart of a genetic algorithm provided by the present invention;

FIG. 7 is a technical diagram of a rolling bearing fault diagnosis method based on a genetic neural network provided by the invention;

FIG. 8 is a schematic diagram of the EMD algorithm provided by the present invention;

FIG. 9 is a schematic diagram of the MEEMD algorithm provided by the present invention;

FIG. 10 is a graph of the rapid spectral kurtosis after the experiments provided by the present invention.

Detailed Description

The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.

In the prior art, a bearing of a bogie of a subway vehicle is usually maintained in two modes, one mode is manual detection, the other mode is that bearing vibration data are collected, and whether a bearing fault characteristic frequency signal exists or not is compared, wherein the former depends on subjective judgment and consumes very much time and labor, and when the latter finds abnormality, the bearing is in the final stage of failure; therefore, the method aims to provide a new method which can meet the diagnosis, classification and health assessment of the weak signal fault of the bearing, and the metro vehicle is a complex multi-body system and has interaction force and relative movement among all parts and interaction relation among wheel tracks; therefore, simulation conforming to the actual situation is required to be performed as much as possible, a mathematical model for coupling the transverse motion and the vertical motion of the vehicle is considered during modeling, and a refined nonlinear dynamic model is established; checking the collected vibration signals of the bearing and the bogie and the response of the dynamic model, and verifying the reliability of the model; the reliable dynamic model is beneficial to further understanding the change of the system state when the fault occurs, and meanwhile, a basis is provided for the arrangement of the vehicle-mounted sensor. The method aims to establish a vehicle system dynamic model, simulate the fault working condition of a bogie bearing, analyze the sensitivity of natural frequency to the rigidity change of the fault bearing, establish a functional relation and analyze the difference between sudden change and vibration enhancement of the system model caused by different fault working conditions of the bearing.

The method comprises the steps that a thinking path of the method is shown in figure 1, vibration data of various working conditions of a bogie bearing of a high-speed rail motor car are collected, the data are distributed in two aspects, one is that a bearing fault diagnosis method is selected, the second is used for evaluating the health, and finally the method is achieved through a program.

Fig. 2 shows a bearing fault diagnosis flowchart, which specifically includes the following steps:

acquiring bearing acceleration signals in a subway running state, and acquiring normal vibration data under different working conditions and vibration data under different fault working conditions; in the step, a series of bearings with faults of the subway vehicle are mainly collected, the types of the faults are gathered, the faults are classified from light to serious, a rolling vibration test and a positive line test are carried out on the faults selected from each type, the vibration state of the bearings and the corresponding bogie state are collected, and the vibration relation of the bearings with different fault degrees to the bogie is tried to be established;

it should be noted that, in the test process, it is found that the rolling bearing faults are mainly divided into two types of surface wear and local damage according to types, when the bearing surface is worn, the vibration replication is gradually increased, and the waveform has no significant change, so that the damage degree of the local damage faults such as pitting corrosion and peeling is relatively much larger;

secondly, extracting the characteristics of the obtained state signals, and decomposing the state signals by an improved ensemble empirical mode decomposition method (MEEMD-PE) to obtain a plurality of intrinsic mode components;

it should be noted that, before feature extraction is performed on the obtained vibration signal, normalization processing needs to be performed, because the directly measured and obtained data is relatively coarse and often contains noise, trend terms and the like, and subsequent analysis cannot be directly performed, in order to obtain a more accurate analysis result, data processing needs to be performed first, and general data processing includes data normalization, denoising, direct current component removal, trend term elimination and the like; the output of the vibration sensor is generally a time waveform, and a time domain signal is converted into a frequency domain signal through Fourier transform;

in this application, assuming that there is no slip between each rolling element and the inner and outer ring surfaces of the rolling bearing, the calculation formula of the failure frequency of the rolling bearing is as follows:

frequency conversion f of bearing _r ：

Frequency of rolling bodies passing outer ring fault point, i.e. outer ring fault characteristic frequency f _o ：

Frequency of rolling bodies passing through inner ring fault point, i.e. inner ring fault characteristic frequency f _i ：

Frequency of damage to rolling elements at a point passing through inner and outer races, i.e. rolling element failure characteristic frequency f _e ：

Characteristic frequency f of failure of cage _c ：

In the formula, the rotating speed of the bearing is n (r/min), the diameter of the rolling bodies is D (mm), the pitch diameter of the bearing is D (mm), the number of the rolling bodies is Z, and the contact angle is alpha.

Some algorithm principles in this application are as follows:

principle of MEEMD algorithm

An Empirical Mode Decomposition (EMD) method is an adaptive data processing method, which decomposes a complex signal into a plurality of eigen-Mode function components (IMF components), but the obtained IMF components often have modal mixing, so as to solve the modal aliasing phenomenon occurring in EMD Decomposition, reduce pseudo components, and eliminate abnormal IMF components by means of multi-component set averaging and Permutation Entropy (PE) identification, thereby improving the method into an ememd method, and the algorithm flow of the method is as shown in fig. 8 and 9.

Rapid spectral kurtosis diagram (shown in figure 10)

The Spectral Kurtosis (SK) method indicates the frequency band range where hidden non-stationary signals appear by calculating the signal Kurtosis value, the band-pass filtering frequency range of each IMF signal is determined by adopting a fast Spectral Kurtosis map (FSK) decomposed by a dichotomy, fast Spectral Kurtosis maps of an original signal and each IMF are respectively calculated, whether the frequency band interval where the maximum value of each IMF Spectral Kurtosis is located is consistent with the frequency band interval of the original signal or not is judged, if so, the IMF is preliminarily considered as a key, and a key IMF component library is obtained by combining a cross correlation coefficient mode, and the example is performed by taking an example in fig. 9.

Mahalanobis distance calculation principle

The Matt system (MTS) is a method for identifying weak faults of a bearing based on multidimensional parameters, and the Ma distance is calculated under the Matt system based on MEEMD so as to represent the health state of the bearing, wherein the calculation formula is as follows:

wherein: n samples are provided, each sample has m characteristic variables, and a reference space matrix formed by the n samples is (X) _ij )n×m，Z _ij To normalize the spatial matrix, x _ij Is a reference spatial matrix.

In the formula:

is mahalanobis distance and R is the signal-to-noise ratio of mahalanobis distance.

In the diagnosis process, the time domain signal and the frequency domain signal are compared and analyzed, the dynamic characteristic of a test object can be known, the state of the equipment is evaluated, the equipment fault is accurately and effectively diagnosed, the fault is checked and positioned, and a basis is provided for avoiding the fault.

In the bearing fault diagnosis process, as shown in fig. 3, regarding the extraction of weak fault signals, in the third step, key eigenmode component signals are selected for original signal reconstruction, the selected limiting conditions are as shown in fig. 3, the kurtosis is greater than 3.5, the first N key eigenmode component signals are selected, N is limited to be less than or equal to 5 again, reconstruction signals are obtained, and band-pass parameters are determined by the reconstruction signals through a fast spectrum kurtosis graph; carrying out improved resonance demodulation processing after the determined band-pass parameter is filtered, carrying out bearing fault diagnosis through a square envelope spectrum, and judging whether a bearing fails;

as shown in fig. 4, the method for determining the health level of the current bearing specifically includes the following steps: carrying out neural network training on the new eigenmode component characteristic set obtained in the third step to form a bearing fault model; identifying the fault type by using the trained bearing fault model; in the method, a trained bearing fault model is used for identifying the fault type, the model is generated based on an intelligent algorithm as shown in fig. 7 as a research means, the input quantity and the output quantity are set by combining the results of early theoretical analysis and online monitoring, a stable prediction model is gradually obtained through continuous adaptive adjustment, and a method is provided for accurate prediction of the fault; forming a reference space by the intrinsic mode component characteristic set, and calculating the Mahalanobis distances of various bearings; constructing a bearing health index and health grade table of [0, 100] by combining the existing Mahalanobis distance and statistical data and historical experience of bearing vibration in the rail industry, namely quantizing the real-time running state by using the health index, setting the health index of the bearing between 0 and 100, wherein the larger the numerical value is, the healthier the bearing state is; when the index is 100, the target bearing is in the best state, when the index is 0, the target bearing completely fails, the health state is graded, and good (85, 100), healthy (70, 85), sub-healthy (55, 70), early warning (40, 55), warning (25, 40) and failure [0, 25] are set, so that as shown in fig. 5, according to different health grades, different active maintenance measures and strategies are adopted, the service life of the rolling bearing can be effectively prolonged, and unnecessary loss can be avoided.

During health assessment, MTS is used as a multi-dimensional pattern recognition method, and the method mainly aims to measure whether a sample to be detected is abnormal or not and the abnormal degree by constructing a measurement scale; compared with other pattern recognition technologies such as a support vector machine, bayesian discrimination, a neural network and the like, the MTS has simple calculation and high operation speed in classification and recognition, and does not involve the hypothesis of parameter estimation and training data distribution, such as prior distribution in a Bayesian network, probability distribution in statistics, and membership degree or membership function in a fuzzy set theory; the MTS combines the Mahalanobis distance and the Taguchi method, fully utilizes the Mahalanobis distance as a reference basis, calculates the distance of a sample to be measured, and reflects the current state of the bearing through the distance.

In the application, a hardware system for acquiring data comprises a power supply, a three-way acceleration sensor positioned at the top end of a bearing, a three-way acceleration sensor positioned at the bottom end of the bearing, a data acquisition card and a computer, wherein the power supply is used for supplying power to the three-way acceleration sensors at the top end and the bottom end of the bearing; the data acquisition card is used for acquiring vibration acceleration values of the bearing in three directions, which are measured by the two three-direction acceleration sensors; the computer is used for receiving the three-direction acceleration value from the data acquisition card, analyzing the three-direction acceleration value, comparing the three-direction acceleration value with a threshold value according to an analysis result and monitoring the state of the bearing in real time; in the test system, a three-way acceleration sensor is adopted, according to the results of dynamic model calculation and modal analysis, the selected layout position is arranged on the upper action surface and the lower action surface of a bearing, the selected three-way acceleration sensor is marked on the sensor in three positive directions, the positive direction of the X axis of the sensor faces the advancing direction of a train when the three-way acceleration sensor is installed, the Y axis direction and the X axis are in the same plane but perpendicular to the X axis, and the Z axis is perpendicular to the horizontal plane of a track.

The online fault diagnosis combines real-time data acquisition, data analysis and fault diagnosis, and judges whether a fault occurs or not by comparing the real-time data analysis with a set threshold value, wherein the operation mode based on the online fault diagnosis comprises two modes, the first mode is a learning mode, and after the fifth step is completed, an identified early weak fault signal is processed, whether a bearing fails or not is judged, the health grade of the current bearing is judged, and the data are stored regularly; the learning mode is suitable for new equipment, new load working conditions, equipment after maintenance and the like, so that the alarm threshold value can be changed according to the new working conditions; the second is an alarm mode, and after the fifth step is completed, the identified early weak fault signal is processed to judge whether the bearing fails or not, judge the health grade of the current bearing and only store the fault data in the bearing; after the system obtains a stable threshold value through a section of learning mode, an alarm mode is used; the algorithm for neural network training in diagnosing or judging the current health level of the bearing based on the reconstructed signal generally adopts a genetic algorithm, and as shown in fig. 6, the algorithm is a schematic flow chart of the genetic algorithm.

In the method, the trained bearing fault model is used for identifying the fault type, the model is generated based on an intelligent algorithm as shown in fig. 7 as a research means, the input quantity and the output quantity are set by combining the results of early theoretical analysis and online monitoring, and a stable prediction model is gradually obtained by continuous adaptive adjustment, so that a method is provided for accurate prediction of the fault.

In summary, the fault diagnosis method based on MEEMD-PE (improved empirical mode decomposition) and a fast spectral kurtosis diagram is provided, and simulation, test and engineering practice show that the method effectively identifies the weak fault of the bogie bearing; the bearing fault detection method can not only accurately identify but also detect the bearing fault in early stage, thereby forming engineering benefits.

Training is carried out through a BP neural network, so that a bearing fault model is established, different bearings can be classified, and different bearing faults can be classified; the type of the bearing fault can be quickly positioned, so that a path causing the fault is analyzed, and the occurrence rate of the fault is reduced on the path; meanwhile, a bearing maintenance strategy and a bearing operation replacement strategy can be formulated through the health evaluation grade, so that the benefits of reducing the replacement cost and saving the general investigation time are achieved.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The meaning of "and/or" as used herein is intended to include both the individual components or both.

The term "connected" as used herein may mean either a direct connection between components or an indirect connection between components through other components.

In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims

1. A method for diagnosing, classifying and evaluating health of weak signal faults of a metro vehicle bogie bearing is characterized by comprising the following steps: the method comprises the following steps:

the method comprises the steps that firstly, bearing acceleration signals in a subway running state are collected, normal vibration data under different working conditions and vibration data under different fault working conditions are obtained, a mathematical model for coupling transverse motion and vertical motion of a vehicle is considered during modeling, and a refined nonlinear dynamics model is established;

the method comprises the steps of discharging abnormal IMF components in a multi-component set averaging and permutation entropy identification mode, improving the method to be a MEEMD method, indicating a frequency band range where hidden non-stationary signals appear by calculating signal kurtosis values, determining a band-pass filtering frequency range of each IMF signal by adopting a fast spectral kurtosis graph method of dichotomy decomposition, respectively calculating fast spectral kurtosis graphs of an original signal and each IMF, judging whether a frequency band interval where each IMF spectral kurtosis maximum value is located is consistent with the original signal frequency band interval, if so, preliminarily considering the IMF as a key, and simultaneously combining a cross-correlation coefficient mode to obtain a key IMF component library;

step 52, utilizing the trained bearing fault model to identify the fault type;

step 53, forming the characteristic feature set of the eigenmode component into a reference space (MS) _N N = 10), indicating that each IMF in the reference space has 10 degrees of freedom, the effective characteristic parameters are: the method comprises the steps of averaging, peak-to-peak values, effective values, peak factors, kurtosis, multi-point kurtosis, skewness, shape coefficients, pulse coefficients and margin factors, and then calculating the Mahalanobis distances of various existing bearings;

the health index of the bearing is set between 0 and 100, and the larger the value is, the healthier the bearing state is; an index of 100 indicates that the target bearing is in an optimal state, an index of 0 indicates that the target bearing is completely failed, and the health state is ranked and set to good (85, 100), healthy (70, 85), sub-healthy (55, 70), early warning (40, 55), warning (25, 40), failed [0, 25].

2. The method for diagnosing, classifying and evaluating the weak signal fault of the bogie bearing of the metro vehicle according to claim 1, wherein: in the fourth step, the diagnosis based on the reconstructed signal includes the steps of:

and step 42, performing improved resonance demodulation processing on the determined band-pass parameters after filtering processing, and performing bearing fault diagnosis through a square envelope spectrum to judge whether the bearing fails.

3. The method for weak signal fault diagnosis, classification and health assessment of a metro vehicle bogie bearing according to claim 1, wherein: the method comprises the following steps that a three-way acceleration sensor is installed at the top end of a bearing to be detected, a three-way acceleration sensor is installed at the bottom end of the bearing to be detected, the two three-way acceleration sensors are powered by a power supply, a data acquisition card acquires vibration acceleration values of the bearing to be detected in three directions, the vibration acceleration values are transmitted to a computer for analysis, and data required in the first step are acquired.

4. The method for weak signal fault diagnosis, classification and health assessment of a metro vehicle bogie bearing according to claim 1, wherein: the method comprises two modes, wherein the first mode is a learning mode, after the fifth step is completed, the recognized early weak fault signal is processed, whether the bearing fails or not is judged, the health grade of the current bearing is judged, and the data are stored regularly;

and the second mode is an alarm mode, and after the fifth step is completed, the identified early weak fault signal is processed to judge whether the bearing fails or not, judge the health grade of the current bearing and only store fault data in the bearing.

5. The method for diagnosing, classifying and evaluating the weak signal fault of the bogie bearing of the metro vehicle according to claim 2, wherein: and (3) adopting a genetic algorithm for carrying out neural network training in diagnosing or judging the health grade of the current bearing based on the reconstructed signal.