|Table of Contents|

Rolling bearing fault diagnosis method based on TQWT and sparse representation(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2021年06期
Page:
237-246
Research Field:
载运工具运用工程
Publishing date:

Info

Title:
Rolling bearing fault diagnosis method based on TQWT and sparse representation
Author(s):
NIU Yi-jie1;  LI Hua2;  DENG Wu3;  FEI Ji-you2;  SUN Ya-li4;  LIU Zhi-bo4
(1. Software Technology Institute, Dalian Jiaotong University, Dalian 116028, Liaoning, China; 2. College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China; 3. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China; 4. School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China)
Keywords:
vehicle engineering;  rolling bearing;  fault diagnosis;  sparse representation;  tunable-Q wavelet transform;  feature extraction
PACS:
U270.1
DOI:
10.19818/j.cnki.1671-1637.2021.06.018
Abstract:
Based on the sparse representation theory, a new method of rolling bearing fault diagnosis was proposed using the tunable-Q wavelet transform(TQWT). The characteristics of the original vibration signals and early fault signals containing early fault components were analyzed, and the applications of the sparse representation model to solve the problem of fault feature extraction and fault type recognition were studied. The original signal was transformed into a set of sub-band wavelet coefficients using the TQWT. The effectiveness of extracting sparse wavelet coefficients using an iterative threshold shrinkage algorithm and the sensitivity of spectral kurtosis to fault impact signals were studied. By calculating the spectral kurtosis of each sub-band signal component and selecting the sub-band wavelet coefficient that contains obvious fault information, a fault feature extraction method for the sparse fault signal component was established. Using the sparse representation classification model of extracted fault signals, the method of rolling bearing fault-type recognition based on sparse representation was realized. Experimental results indicate that the proposed fault feature extraction method has a significant effect in eliminating interference components in the Case Western Reserve University dataset. The average diagnostic accuracy for the four types of data is 99.83%. The average diagnostic accuracy for the 10 types of data is 97.73%. Compared with the TQWT and iterative threshold shrinkage algorithm for fault feature extraction, the fault diagnosis accuracy of the proposed method improves by 11.60%, and the running time reduces by 8%. For the vibration dataset collected by the QPZZ-Ⅱ rotating machinery platform, the average diagnostic accuracy of the proposed method for the four types of data is 100%. Compared with the traditional wavelet denoising method, the accuracy of the proposed method improves by 35.67%, and the running time reduces by 7.25%. Therefore, the proposed method can effectively solve the problem of rolling-bearing fault diagnosis. 7 tabs, 7 figs, 30 refs.

References:

[1] NANDI S, TOLIYAT H A, LI X. Condition monitoring and fault diagnosis of electrical motors—a review[J]. IEEE Transactions on Energy Conversion, 2005, 20(4): 719-729.
[2] 王国彪,何正嘉,陈雪峰,等.机械故障诊断基础研究“何去何从”[J].机械工程学报,2013,49(1):63-72.
WANG Guo-biao, HE Zheng-jia, CHEN Xue-feng, et al. Basic research on machinery fault diagnosis—what is the prescription[J]. Journal of Mechanical Engineering, 2013, 49(1): 63-72.(in Chinese)
[3] 沈长青,王 旭,王 冬,等.基于多尺度卷积类内迁移学习的列车轴承故障诊断[J].交通运输工程学报,2020,20(5):151-164.
SHEN Chang-qing, WANG Xu, WANG Dong, et al. Multi-scale convolution intra-class transfer learning for train bearing fault diagnosis[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 151-164.(in Chinese)
[4] 马小骏,任淑红,左洪福,等.基于LS-SVM算法和性能可靠性的航空发动机在翼寿命预测方法[J].交通运输工程学报,2015,15(3):92-100.
MA Xiao-jun, REN Shu-hong, ZUO Hong-fu, et al. Prediction method of aero-engine life on wing based on LS-SVM algorithm and performance reliability[J]. Journal of Traffic and Transportation Engineering, 2015, 15(3): 92-100.(in Chinese)
[5] KLEIN R, INGMAN D, BRAUN S. Non-stationary signals: phase-energy approach—theory and simulations[J]. Mechanical Systems and Signal Processing, 2001, 15(6): 1061-1089.
[6] FENG Zhi-peng, LIANG Ming, CHU Fu-lei. Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples[J]. Mechanical Systems and Signal Processing, 2013, 38(1): 165-205.
[7] ANTONI J. Cyclostationarity by examples[J]. Mechanical Systems and Signal Processing, 2009, 23(4): 987-1036.
[8] LEI Ya-guo, HE Zheng-jia, ZI Yan-yang. Application of the EEMD method to rotor fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2009, 23(4): 1327-1338.
[9] 李奕璠,刘建新,林建辉,等.基于自适应多尺度形态学分析的车轮扁疤故障诊断方法[J].交通运输工程学报,2015,15(1): 58-65.
LI Yi-fan, LIU Jian-xin, LIN Jian-hui, et al. Fault diagnosis method of railway vehicle with wheel flat based on self-adaptive multi-scale morphology analysis[J]. Journal of Traffic and Transportation Engineering, 2015, 15(1): 58-65.(in Chinese)
[10] YAN R Q, GAO R X, CHEN X F. Wavelets for fault
diagnosis of rotary machines: a review with applications[J]. Signal Processing, 2014, 96: 1-15.
[11] 张志禹,吕延军,张九龙,等.航空发动机转子碰摩故障瞬时频率诊断方法[J].交通运输工程学报,2007,7(4):21-23.
ZHANG Zhi-yu, LYU Yan-jun, ZHANG Jiu-long, et al. Diagnosis method of instantaneous frequency for rotor impact-rub fault of aeroengine[J]. Journal of Traffic and Transportation Engineering, 2007, 7(4): 21-23.(in Chinese)
[12] SELESNICK I W. Wavelet transform with tunable Q-factor[J]. IEEE Transactions on Signal Processing, 2011, 59(8): 3560-3575.
[13] LIU Ruo-nan, YANG Bo-yuan, ZIO E, et al. Artificial
intelligence for fault diagnosis of rotating machinery: a review[J]. Mechanical Systems and Signal Processing, 2018, 108: 33-47.
[14] ZHANG Shen, ZHANG Shi-bo, WANG Bin-guan, et al.
Deep learning algorithms for bearing fault diagnostics—a comprehensive review[J]. IEEE Access, 2020, 8: 29857-29881.
[15] 高 倩,陈晓英,孙丽颖.基于稀疏表示的TQWT在低频振荡信号去噪中应用[J].电力系统保护与控制,2016,44(13):55-60.
GAO Qian, CHEN Xiao-ying, SUN Li-ying. Low frequency oscillating signals denoising based on TQWT via sparse representation[J]. Power System Protection and Control, 2016, 44(13): 55-60.(in Chinese)
[16] 王 霄,谢 平,郭源耕,等.基于多字典-共振稀疏分解的脉冲故障特征提取[J].中国机械工程,2019,30(20):2456-2462,2472.
WANG Xiao, XIE Ping, GUO Yuan-geng, et al. Impulse fault signature extraction based on multi-dictionary resonance-based sparse signal decomposition[J]. China Mechanical Engineering, 2019, 30(20): 2456-2462, 2472.(in Chinese)
[17] CAI Gai-gai, CHEN Xue-feng, HE Zheng-jia. Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox[J]. Mechanical Systems and Signal Processing, 2013, 41(1/2): 34-53.
[18] ZHAO Zhi-bin, WANG Shi-bin, AN Bo-tao, et al. Hierarchical hyper- laplacian prior for weak fault feature enhancement[J]. ISA Transactions, 2020, 96: 429-443.
[19] 赵见龙,张永超,王立夫,等.基于共振稀疏分解与谱峭度的滚动轴承故障诊断[J].组合机床与自动化加工技术,2019(4):111-115.
ZHAO Jian-long, ZHANG Yong-chao, WANG Li-fu, et al. Rolling bearing fault diagnosis based on resonance sparse decomposition and spectral kurtosis[J]. Modular Machine Tool and Automatic Manufacturing Technique, 2019(4): 111-115.(in Chinese)
[20] 孔 运,王天杨,褚福磊.自适应TQWT滤波器算法及其在冲击特征提取中的应用[J].振动与冲击,2019,38(11): 9-16,23.
KONG Yun,WANG Tian-yang,CHU Fu-lei. Adaptive TQWT filter algorithm and its application in impact feature extraction[J]. Journal of Vibration and Shock, 2019, 38(11): 9-16, 23.(in Chinese)
[21] LI Jun-lin, WANG Hua-qing, SONG Liu-yang, et al. A novel feature extraction method for roller bearing using sparse decomposition based on self-adaptive complete dictionary[J]. Measurement, 2019, 148: 106934.
[22] FENG Z P, ZHOU Y K, ZUO M J, et al. Atomic
decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: a review with examples[J]. Measurement, 2017, 103: 106-132.
[23] 王宏超,陈 进,董广明,等.可调品质因子小波变换在转子早期碰摩故障诊断中应用[J].振动与冲击,2014,33(10):77-80.
WANG Hong-chao, CHEN Jin, DONG Guang-ming, et al. Early rub-impact diagnosis of rotors based on tunable Q-factor wavelet transformation[J]. Journal of Vibration and Shock, 2014, 33(10): 77-80.(in Chinese)
[24] BECK A, TEBOULLE M. A fast iterative shrinkage-
thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183-202.
[25] 何正嘉,訾艳阳,陈雪峰,等.内积变换原理与机械故障诊断[J].振动工程学报,2007,20(5):528-533.
HE Zheng-jia, ZI Yan-yang, CHEN Xue-feng, et al. Transform principle of inner product for fault diagnosis[J]. Journal of Vibration Engineering, 2007, 20(5): 528-533.(in Chinese)
[26] 苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.
SU Wen-sheng, WANG Feng-tao, ZHANG Zhi-xin, et al. Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J]. Journal of Vibration and Shock, 2010, 29(3): 18-21.(in Chinese)
[27] ANTONI J. The spectral kurtosis: a useful tool for characterising non-stationary signals[J]. Mechanical Systems and Signal Processing, 2006, 20(2): 282-307.
[28] WRIGHT J, YANG A Y,GANESH A, et al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[29] LI Yong-bo, WANG Xian-zhi, SI Shu-bin, et al. Entropy
based fault classification using the case western reserve university data: a benchmark study[J]. IEEE Transactions on Reliability, 2020, 69(2): 754-767.
[30] SU W S, WANG F T, ZHU H, et al. Rolling element
bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement[J]. Mechanical Systems and Signal Processing, 2010, 24(5): 1458-1472.

Memo

Memo:
-
Last Update: 2021-12-20