The Methodology of Modified Frequency Band Envelope Kurtosis for Bearing Fault Diagnosis

被引:17
作者
Hua, Li [1 ]
Wu, Xing [2 ,3 ]
Liu, Tao [3 ]
Li, Shaobo [1 ,4 ]
机构
[1] Guizhou Univ, State Key Lab Publ Big Data, Guiyang 550025, Peoples R China
[2] Yunnan Vocat Coll Mech & Elect Technol, Kunming 650203, Peoples R China
[3] Kunming Univ Sci & Technol, Fac Mech & Elect Engn, Kunming 650500, Peoples R China
[4] Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China
基金
中国国家自然科学基金;
关键词
Time-frequency analysis; Resonant frequency; Bandwidth; Transforms; Entropy; Noise reduction; Band-pass filters; Band-pass filter; correlation coefficient; envelope signal kurtosis; fault diagnosis; modified adaptive resonance bandwidth (MARB); time-frequency distribution; WAVELET PACKET TRANSFORM; DECOMPOSITION; EXTRACTION; SIGNALS;
D O I
10.1109/TII.2022.3183548
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recently, the enhanced frequency band entropy (EFBE) was proposed based on the replacement of short-time Fourier transform with wavelet packet transform. In view of the shortcomings of EFBE, a feasible solution is provided, namely modified frequency band envelope kurtosis (MFBEK), which can be described as follows: First, the modified adaptive resonance bandwidth (MARB) based on the bearing inner-race fault frequency is proposed. The kurtosis of the envelope signal as an available indicator and the MARB are used to determine the optimal depth of MFBEK. Second, this special case, that is, the resonant frequency occurs at the junction of two adjacent subbands is considered, and a corresponding effective solution is provided to determine the optimal subband(s). Then, the reconstructed signal can be obtained. And then, if necessary, a band-pass filter is designed to process the reconstructed signal to enhance the noise reduction performance. Finally, envelope power spectrum analysis is performed on the reconstructed signal or the filtered signal to extract the fault characteristic frequency. In addition, a modified indicator is proposed to measure the analysis results. Analysis results on simulated and vibration signals measured from actual bearing have revealed that the MFBEK can obtain more robust performance.
引用
收藏
页码:2856 / 2865
页数:10
相关论文
共 29 条
[11]   Research on test bench bearing fault diagnosis of improved EEMD based on improved adaptive resonance technology [J].
Li, Hua ;
Liu, Tao ;
Wu, Xing ;
Li, Shaobo .
MEASUREMENT, 2021, 185 (185)
[12]   A Bearing Fault Diagnosis Method Based on Enhanced Singular Value Decomposition [J].
Li, Hua ;
Liu, Tao ;
Wu, Xing ;
Chen, Qing .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (05) :3220-3230
[13]   Enhanced Frequency Band Entropy Method for Fault Feature Extraction of Rolling Element Bearings [J].
Li, Hua ;
Liu, Tao ;
Wu, Xing ;
Chen, Qing .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (09) :5780-5791
[14]   Application of optimized variational mode decomposition based on kurtosis and resonance frequency in bearing fault feature extraction [J].
Li, Hua ;
Liu, Tao ;
Wu, Xing ;
Chen, Qing .
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2020, 42 (03) :518-527
[15]   Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy [J].
Li, Hua ;
Liu, Tao ;
Wu, Xing ;
Chen, Qing .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 118 :477-502
[16]  
Li H, 2017, 2017 9TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC 2017), P320, DOI 10.1109/ICMIC.2017.8321661
[17]   The fault detection and diagnosis in rolling element bearings using frequency band entropy [J].
Liu, Tao ;
Chen, Jin ;
Dong, Guangming ;
Xiao, Wenbing ;
Zhou, Xuning .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2013, 227 (C1) :87-99
[18]   Multiwavelet Packet Entropy and its Application in Transmission Line Fault Recognition and Classification [J].
Liu, Zhigang ;
Han, Zhiwei ;
Zhang, Yang ;
Zhang, Qiaoge .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (11) :2043-2052
[19]   Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals [J].
Mandic, Danilo P. ;
Rehman, Naveed Ur ;
Wu, Zhaohua ;
Huang, Norden E. .
IEEE SIGNAL PROCESSING MAGAZINE, 2013, 30 (06) :74-86
[20]  
Miller F. P., 2011, EULERS FORMULA