CN110207987B

CN110207987B - Method for judging performance degradation decline node of rolling bearing

Info

Publication number: CN110207987B
Application number: CN201910394803.2A
Authority: CN
Inventors: 赵慧敏; 刘浩东; 邓武
Original assignee: Civil Aviation University of China
Current assignee: Civil Aviation University of China
Priority date: 2019-05-13
Filing date: 2019-05-13
Publication date: 2020-12-15
Anticipated expiration: 2039-05-13
Also published as: CN110207987A

Abstract

The invention discloses a method for judging a degradation node of rolling bearing performance degradation, which relates to the technical field of bearing performance detection. According to the invention, in the whole bearing life cycle, the extracted performance degradation characteristic quantities of different bearings are consistent, and the working stage of the bearing and the node entering the decline period can be judged according to the trend curve, so that the predictable interval can be determined. In addition, the method can effectively improve the precision of the prediction of the residual life of the rolling bearing, and provides a method basis for ensuring the safety, the availability and the efficient work of the system, reducing the maintenance cost and realizing the state maintenance.

Description

Method for judging performance degradation decline node of rolling bearing

Technical Field

The invention relates to the technical field of bearing performance detection, in particular to a method for judging a rolling bearing performance degradation decline node.

Background

Different bearings have certain differences in service life values due to different working conditions, damage types and the like, and indexes reflecting the consistency of the performance degradation characteristic quantity change trends of the different bearings are difficult to find, so that the difficulty is brought to improving the prediction accuracy of the residual service life of the bearings. The whole working cycle of the bearing can be divided into a running-in stage, a normal stage and a decline stage. The residual life of the bearing is predicted by selecting a performance degradation period as a prediction interval, and if an index reflecting the consistency of the change trend of the extracted performance degradation characteristic quantity cannot be found, the working period division of the bearing has no unified standard. Therefore, it is important to find an index reflecting the consistency of the change trend of the extracted characteristic quantity, the prediction precision of the residual life of the bearing can be improved, and a uniform standard can be provided for determining the decay period.

Disclosure of Invention

In order to solve the problems in the background art, the invention provides a method for judging the performance degradation decline nodes of the rolling bearing, which can enable the change trends of the performance degradation characteristic quantities of different extracted bearings to be consistent in the whole life cycle, judge the working stage of the bearing and the nodes entering the decline period according to a trend curve and further determine a predictable interval.

The technical scheme of the invention is as follows:

a method for judging a rolling bearing performance degradation decline node comprises the following specific steps:

step 1: obtaining vibration acceleration data of different monitoring periods, and performing Hilbert transform on data samples Dataj in a bearing full-life data set bi to obtain the power spectral density PSD _ Dataj of the Dataj;

step 2: obtaining the maximum value PSD _ MAX _ Dataj of a power spectrum density curve PSD _ Dataj of the data Dataj, and forming a power spectrum density maximum value set PMDI (PSD _ MAX _ Dataj | i is 1,2, …, n, j is 1,2, … and Ni), wherein n is the number of bearings contained in a bearing full-life data set bi, and Ni is the number of data samples in bi;

and step 3: performing curve fitting on the power spectral density maximum value set PMDI by using fourth-order polynomial fitting to obtain a fitting curve PPMDi;

and 4, step 4: performing derivation on the fitting curve PPMDi to obtain a derivation curve DPPMDI of the fitting curve, namely the characteristic quantity change trend;

and 5: and solving inflection points of the DPPMDI curve, defining the first inflection point as a node of the bearing entering a normal working state, defining the second inflection point as a node of the bearing entering a performance degradation stage, and determining a predictable interval.

Preferably, the derivative curve DPPMDi is the power spectral density maximum first derivative curve.

The method for judging the rolling bearing performance degradation decline node has the beneficial effects that: in the whole bearing life cycle, the performance degradation characteristic quantity change trends of different extracted bearings can be consistent, the working stage of the bearing and the node entering the decline period can be judged according to the trend curve, and a predictable interval can be further determined. In addition, the method can effectively improve the precision of the prediction of the residual life of the rolling bearing, and provides a method basis for ensuring the safety, the availability and the efficient work of the system, reducing the maintenance cost and realizing the state maintenance.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a specific flowchart of a method for determining a rolling bearing performance degradation node according to the present invention;

FIG. 2 is a graph of the life-cycle performance degradation characteristic of the bearing of the present invention; wherein the content of the first and second substances,

a (1) is a bearing1_1 power spectral density maximum curve and a fitted curve thereof, and a (2) is a first derivative of the fitted curve;

b (1) is a bearing1_2 power spectral density maximum curve and a fitted curve thereof, b (2) is a first derivative of the fitted curve;

c (1) is a bearing1_5 power spectral density maximum curve and a fitted curve thereof, and c (2) is a first derivative of the fitted curve;

d (1) is the bearing1_7 power spectral density maximum curve and its fitted curve, d (2) is the first derivative of the fitted curve;

e (1) bearing2_1 power spectral density maximum curve and its fitted curve, e (2) is the first derivative of the fitted curve;

f (1) is a bearing2_2 power spectral density maximum curve and a fitted curve thereof, and f (2) is a first derivative of the fitted curve;

g (1) is a bearing2_5 power spectral density maximum curve and a fitted curve thereof, and g (2) is a first derivative of the fitted curve;

h (1) is a bearing2_6 power spectral density maximum curve and a fitted curve thereof, and h (2) is a first derivative of the fitted curve;

i (1) is the bearing2_7 power spectral density maximum curve and its fitted curve, i (2) is the first derivative of the fitted curve;

j (1) is the bearing3_1 power spectral density maximum curve and its fitted curve, j (2) is the first derivative of the fitted curve;

k (1) is the bearing3_2 power spectral density maximum curve and its fitted curve, and k (2) is the first derivative of the fitted curve.

FIG. 3 is a graph of the life cycle characteristic of a bearing according to the different feature extraction methods of the present invention; wherein the content of the first and second substances,

(a)1 is bearing1_1 full-life time-domain plot, (a)2bearing1_2 full-life time-domain plot;

(b)1 is bearing1_1 HOMME graph, (b)2 is bearing1_2 HOMME graph;

(c)1 is bearing1_1RMS plot, (c)2 is bearing1_2RMS plot;

(d)1 is bearing1_1EE graph, (d)2 is bearing1_2EE graph;

(e)1 is bearing1_1PMD graph, and (e)2 is bearing1_2PMD graph.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

1 method of experiment

Referring to fig. 1, an embodiment of the present invention provides a method for determining a rolling bearing performance degradation node, where the method includes the following specific steps:

and 4, step 4: performing derivation on the fitting curve PPMDi to obtain a derivation curve DPPMDI of the fitting curve, namely the characteristic quantity change trend; wherein the derivative curve DPPMDi is the power spectral density maximum first derivative curve;

2 course of experiment

According to the method for judging the performance degradation decline node of the rolling bearing, provided by the invention, the bearing degradation data is collected by using an experimental platform of a PRONOSTIA laboratory of the FEMTO-ST research institute. The bearing degradation data are collected by adopting vibration acceleration sensors which form an angle of 90 degrees with each other, the first sensor is placed in the vertical direction of the bearing, the second sensor is placed in the horizontal direction of the bearing, the two sensors are radially placed on an outer seat ring of the bearing, the sampling frequency is 25.6kHz, and 2560 data samples are collected every 10 seconds. The experimental data used in the invention are data collected by a sensor in the vertical direction.

The three operating conditions for the experiment were: (1)1800rpm and 4000N; (2)1650rpm and 4200N;

(3)1500rpm and 5000N.

The bearings were subjected to life tests under different operating conditions, with specific assignments as shown in table 1.

TABLE 1 bearing Life data set

Taking bearing1_1 as an example, the vibration acceleration data of the first monitoring period is collected as shown in table 2.

TABLE 2 vibration acceleration data of bearing1_1 in the first monitoring cycle

And obtaining the power spectral density after performing Hilbert transform on the vibration acceleration data of each monitoring period.

For bearing1_1 as an example, the power spectral density value of the vibration acceleration data of the first monitoring period is shown in table 3.

TABLE 3 bearing1_1 Power spectral Density values for the first monitored cycle of vibratory acceleration data

And carrying out maximum value operation on the power spectral density of the vibration acceleration data in each monitoring period.

Taking bearing1_1 as an example, the power spectral density maximum of the whole life cycle vibration acceleration data is shown in table 4.

TABLE 4 Power spectral Density maxima for bearing1_1 full life cycle vibration acceleration data

3 results and analysis of the experiments

According to a specific algorithm flow of a performance evaluation model, a power spectrum density maximum value curve, a 4-time fitting curve and a first derivative curve of the 4-time fitting curve are made for the full-life data of all the bearings, the full-life cycle characteristic curves of all the bearings are shown in fig. 2, and as can be seen from fig. 2, the Hilbert spectra of all the bearings present different forms in the full-life cycle, the forms of the four-time fitting curves are different, but the first derivatives of the four-time fitting curves present similar forms, and two obvious inflection points appear. Therefore, the new rolling bearing performance evaluation model provided by the invention can enable the extracted performance degradation characteristic quantity change trends of different bearings to be consistent in the whole life cycle, and can judge the working stage of the bearing and the node entering the decline stage according to the trend curve so as to determine the predictable interval.

Different performance degradation characteristic index comparison analysis

In order to verify the effectiveness of the judging method for the rolling bearing performance degradation nodes, 4 performance degradation characteristic indexes of time domain and high-order mathematical morphology spectrum entropy (HONME), Root mean square value (RMS) and Energy Entropy (EE) are compared with PMD indexes, bearing1_1 and bearing1_2 are selected as analysis objects, and the comparison result is shown in FIG. 3.

Two bearings, 5 bearing life characteristics curve diagrams are listed in this experiment. FIG. 3(a) is a time domain plot of the vibration acceleration over the life cycle of the bearing, from which it can be seen that the vibration acceleration amplitude of the bearing1_1 gradually increases over time until eventually failing; the curve form of the bearing1_2 is obviously different from that of the bearing1_1, a large number of sudden changes exist on the curve, and the change of the amplitude does not have an obvious rule, which indicates that a large number of noises exist in the vibration; FIG. 3(b) is a spectral entropy chart of a higher-order mathematical morphology of a bearing, wherein it can be seen that the entropy value of bearing1_1 increases from the beginning to the damage, while the change of the entropy value of bearing1_2 is not obvious, and the trend change is basically not seen, which shows that the HOMSE curve is sensitive to noise; FIG. 3(c) is a plot of the RMS values of the bearings, from which it can be seen that the RMS value of bearing1_1 gradually increases until the bearing is damaged, while there is a significant abrupt change in the RMS amplitude of bearing1_2, which is also caused by the presence of a large amount of noise in the collected signals; FIG. 3(d) is a bearing EE graph, from which it can be seen that the bearing1_1 graph is high in the middle and low on both sides, while the bearing1_2 graph is covered by a large number of abrupt changes. By synthesizing time domain, HOMSE, RMS and EE curve graphs, experimental results show that the 4 characteristics are sensitive to noise and can not shield the influence of the noise on the curve graphs; unlike the above characteristic curves, fig. 3(e) is a bearing PMD graph, from which it can be seen that there are significant similarities for the bearing1_1, bearing1_2 graphs, the general trend of which is similar, there are two significant inflection points, and as the bearing enters the fading period, its value gradually increases until finally failing.

The above disclosure is only for the specific embodiment of the present invention, but the embodiment of the present invention is not limited thereto, and any variations that can be made by those skilled in the art should fall within the scope of the present invention.

Claims

1. A method for judging a rolling bearing performance degradation node is characterized by comprising the following specific steps:

and 4, step 4: performing derivation on the fitting curve PPMDi to obtain a derivation curve DPPMDI of the fitting curve, namely the characteristic quantity change trend; the derivative curve DPPMDi is a first derivative curve of the power spectral density maximum value fitting curve PPMDi;