Fault Feature Extraction Method of Rolling Bearings Based on FIF-CYCBD

NAVIGATE

Table of Contents

STATISTICS

Viewed1444

Downloads1008

Fault Feature Extraction Method of Rolling Bearings Based on FIF-CYCBD

[HTML] PDF下载 (1008)

[1]LIU Yang,LI Lingjun,WANG Yu,et al.Fault Feature Extraction Method of Rolling Bearings Based on FIF-CYCBD[J].Journal of Zhengzhou University (Engineering Science),2022,43(04):35-40.[doi:10.13705/j.issn.1671-6833.2022.01.004]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 04 Page number: 35-40 Column: Public date: 2022-07-03

Title:: Fault Feature Extraction Method of Rolling Bearings Based on FIF-CYCBD

Author(s):: LIU Yang; LI Lingjun; WANG Yu; WANG Junshuo; CAO Yalei; School of Mechanical and Power Engineering，Zhengzhou University，Zhengzhou 450001，China

Keywords:: fast iterative filter decomposition(FIF); maximum second-order cyclostationarity blind deconvolution(CYCBD); rolling bearing; feature extraction; cyclic frequency

CLC:: TH133. 3

DOI:: 10.13705/j.issn.1671-6833.2022.01.004

Abstract:: Because of the complexity of working conditions,it is difficult to extract fault features from vibration signal of the rolling bears.In order to address this problem,a fault feature extraction method based on fast iterative filter decomposition (FIF) and maximum second-order cyclostationarity blind deconvolution (CYCBD) method was proposed.Firstly,the fault signal of the rolling bearing was decomposed by FIF to obtain a series of intrinsic mode function.The components with the correlation coefficient of the source signal greater than 0.6 were reconstructed,and the appropriate cycle frequency was set according to the decomposition result obtained by FIF.Then the CYCBD method was used to unmix and denoise the reconstructed signal.Finally,the processed signal was envelope demodulated to successfully extract the fault features.Compared with the prominent noise component in the signal,the amplitude of fault characteristic frequency obtained by processing was higher than that of noise.Therefore,the method proposed in this paper could effectively realize the extraction of bearing fault frequency and its frequency doubling characteristics.

References:: [1] 赵磊,张永祥,朱丹宸. 复杂装备滚动轴承的故障诊断与预测方法研究综述 [ J] . 中国测试, 2020, 46 (3) :17-25.
ZHAO L, ZHANG Y X, ZHU D C. Review on rolling bearing fault diagnosis and prognostic for complex equipment[ J] . China measurement & test, 2020, 46 (3) : 17-25.

[2] ZHU D C,ZHANG Y X,LIU S Y,et al. Adaptive combined HOEO based fault feature extraction method for rolling element bearing under variable speed condition [ J ] . Journal of mechanical science and technology, 2018,32(10) :4589-4599.

[3] SUSANTO A,LIU C H,YAMADA K,et al. Application of Hilbert-Huang transform for vibration signal analysis in end-milling [ J ] . Precision engineering, 2018, 53: 263-277.

[4] 雷文平,宋圣霖,郝旺身,等. 基于 FV-FBE 的滚动轴承故障诊断研究 [ J] . 郑州大学学报 ( 工学版 ) , 2020,41(5) :82-86.

LEI W P, SONG S L, HAO W S, et al. Fault diagnosis of rolling bearing based on FV-FBE[ J]. Journal of Zhengzhou university ( engineering science), 2020, 41 (5): 82-86.

[5] 雷文平, 吴小龙, 陈超宇, 等. 基于自动编码器和 SVM 的轴承故障诊断方法[ J] . 郑州大学学报 ( 工学版) ,2018,39(5) :68-72.

LEI W P, WU X L, CHEN C Y, et al. The application of SVM based on auto-encoder in bearing fault diagnosis [ J ] . Journal of Zhengzhou university ( engineering science) , 2018, 39(5) : 68-72.

[6] WIGGINS R A. Minimum entropy deconvolution [ J] . Geoexploration,1978,16(1 / 2) :21-35.

[7] ENDO H,RANDALL R B. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter [ J] . Mechanical systems and signal processing,2007, 21(2) :906-919.

[8] BUZZONI M,ANTONI J,D′ELIA G. Blind deconvolution based on cyclostationarity maximization and its application to fault identification [ J ] . Journal of sound and vibration,2018,432:569-601.

[9] CICONE A,ZHOU H M. Numerical analysis for iterative filtering with new efficient implementations based on FFT[ J] . Numerische mathematik,2021,147( 1) :1 -28.

[10] LIN L,WANG Y,ZHOU H M. Iterative filtering as an alternative algorithm for empirical mode decomposition [ J] . Advances in adaptive data analysis,2009,1( 4) : 543-560.

[11] RAAD A, ANTONI J, SIDAHMED M. Indicators of cyclostationarity: theory and application to gear fault monitoring[ J] . Mechanical systems and signal processing,2008,22(3) :574-587. [12] Case Western Reserve University. Bearings vibration data set[EB / OL] . [ 2021- 02- 21] . https: / / engineering. case. edu / bearingdatacenter.

Similar References:

Memo

Last Update: 2022-07-03