Paper Infomation
Research on Feature Extraction and Classification Method of Vibration Signal of Escalator Sprocket Bearing
Full Text(PDF, 4355KB)
Author: Deyang Liu, Yuhang Su, Ningxiang Yang, Jianxun Chen, Jicheng Li
Abstract: In order to improve the accuracy of escalator sprocket bearing fault diagnosis, the problem of the feature extraction method of bearing vibration signal is addressed. In this paper, empirical mode is used to decompose the original signal, and the optimal modal component among the multiple modal components is obtained after the optimization decomposition is selected by the envelope spectrum method, and the multi-angle feature measure is introduced to extract the fault characteristic value. According to the vibration characteristics of the bearing vibration signal data, a bearing signal feature group that is more inclined to the fault feature category information is established, which avoids the absolute problem of extracting a single metric feature. The fuzzy C-means clustering algorithm is used to cluster the sample data with similar characteristics into the same cluster area, which effectively solves the problem that a single measurement analysis cannot characterize the complex internal characteristics of the bearing vibration signal.
Keywords: Bearing, Vibration, Multi-Angle Feature Measurement, Signal Feature Group, Empirical Mode, Fuzzy C-Means Clustering
References:
[1] MA Yonghui,ZOU Xingguang.Cause analysis of a high voltage motor bearing damage accident[J].Huadian Technology,2016,38(02):39-40+78.
[2] YE Liang,XIA Xingtao,CHANG Zhen.Evaluation of Grey Relation between Chaotic Characteristic and Uncertainty of Rolling Bearing Vibration Performance[J]. Mechanical Transmission,2021,45(01):9-16+103.
[3] YAN Fenggang,WANG Jun,LIU Shuai,JIN Ming,SHEN Yi.SVD-Based Low-Complexity Methods for Computing the Intersection of K≥2 Subspaces[J].Chinese Journal of Electronics,2019,28(02):430-436.
[4] XIA Xintao,YE Liang,CHANG Zhen,et al.Prediction for variation process of reliability on vibration performance of rolling bearings under the condition of poor information[J]. Journal of Vibration and Shock,2017,36(8)：105-112.
[5] Ioan Raşa. Complete monotonicity of some entropies[J]. Periodica Mathematica Hungarica, 2017,75(2): 159-166.
[6] Kun Yu and Tian Ran Lin and Ji Wen Tan. A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering[J]. Applied Acoustics, 2017, 121: 33-45.
[7] TANG Yunbing,GAO Deping,LUO Guihuo.Non-linear bearing force of the rolling ball bearing and its influence on vibration of bearing system[J].Journal of Aerospace Power,2006,21(2)：366-373.LI Hongru.YU He,TIAN Zaike,et al.Degradation trend prediction of rolling bearings based on two-element multiscale entropy[J].China Mechanical Engineering,2017(20)：2420-2425.
[8] SINOU J J.Non-linear dynamics and contacts of an unbalanced flexible rotor supported on ball bearings[J]. Mechanism and Machine Theory,2009,44(9)：1713-1732.
[9] XIA X T,MENG F N.Grey relational analysis of measure for uncertainty of rolling bearing friction torque as time series[J]Journal of Grey System,2011,23(2)：135-144.
[10] CHEN L,XIA X T,ZHENG H T,et al.Chaotic dynamics of cage behavior in a high-speed cylindrical roller bearing[J]. Shock and Vibration,2016(3)：1-12.
[11] ZHANG Zhongyun,WU Jiande,MA Jun.et al.Slight fault diagnosis for rolling bearing based on chaos and fractal theory[J].Journal of Central South University)Science and Technology),2016,47(2)：640-646.