A study on a weak fault detection method based on adaptive parametric dictionary design using the Morlet wavelet
DENG Feiyue1,2,QIANG Yawen2,HAO Rujiang2,MA Huaixiang2,GAO Fei2
1. State Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety，Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
2. School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Aiming at the problem that weak fault feature is difficult to detect under strong background noise, an adaptive parametric dictionary design method was proposed. The proposed method is based on the idea of local segmentation and global analysis of the analyzed signal, and two indexes, correlation function (CF) and kurtosis, were used to evaluate the local matching degree and global matching degree between the Morlet wavelet and the fault signal. Moreover, the whale optimization algorithm (WOA) was introduced to the analysis process, and wavelet parameters could be identified automatically. Then the parametric dictionary was constructed by point-by-point time-shift, and the fault feature was detected by the sparse decomposition using the orthogonal matching pursuit (OMP). The proposed method was applied to analyze a simulated fault signal and gear fault signal, the results show that this method can extract weak fault feature effectively, and the diagnosis effect is better than the classical parametric dictionary based on a correlation filtering algorithm (CFA), wavelet denoising,and based on K-SVD learning dictionary.
邓飞跃1，2，强亚文2，郝如江2，马怀祥2，高飞2. 基于自适应Morlet小波参数字典设计的微弱故障检测方法研究[J]. 振动与冲击, 2021, 40(8): 187-193.
DENG Feiyue1,2,QIANG Yawen2,HAO Rujiang2,MA Huaixiang2,GAO Fei2. A study on a weak fault detection method based on adaptive parametric dictionary design using the Morlet wavelet. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(8): 187-193.
［1］古莹奎，曾磊，张敏,等. 基于CEEMDAN-SQI-SVD的齿轮箱局部故障特征提取［J］. 仪器仪表学报, 2019, 40(5):78-88.
GU Yingkui, ZENG Lei, ZHANG Min, et al. Feature extraction method for gearbox local fault based on CEEMDAN-SQI-SVD［J］. Chinese Journal of Scientific Instrument, 2019, 40(5):78-88.
［2］ZHANG K, CHEN X M, LIAO L D, et al. A new rotating mechineary fault diagnosis method based on local oscillatory-characteristic decomposition［J］. Digital Signal Processing, 2018, 78:98-107.
［3］佘博，田福庆，梁伟阁,等. 增量式监督局部切空间排列算法及齿轮箱故障诊断实验验证［J］. 振动与冲击, 2018, 37(13):105-110.
SHE Bo, TIAN Fuqing, LIANG Weige, et al. Test verification for gearbox fault diagnosis based on incremental supervised local tangent space alignment algorithm［J］. Journal of Vibration and Shock, 2018, 37(13):105-110.
［4］DING J M, ZHAO W T, MIAO B R, et al. Adaptive sparse representation based on circular-structure dictionary learning and its application in wheelset-bearing fault dection［J］.Mechanical Systems and Signal Processing, 2018, 111: 399-422.
［5］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.
［6］郭俊锋，石斌，魏兴春,等. 基于 K-SVD 字典学习算法的稀疏表示振动信号压缩测量重构方法［J］. 机械工程学报, 2017, 54(7)：97-106.
GUO Junfeng, SHI Bin, WEI Xingchun, et al. A method of reconstruction of compressed measuring for mechanical vibration signals based on K-SVD dictionary-training algorithm［J］. Journal of Mechanical Engineering, 2017, 54(7): 97-106.
［7］RUBINSTEIN R, ZIBULEVSKY M, ELAD M. Double sparsity: learning sparse dictionaries for sparse signal approximation［J］.IEEE Transactions on Signal Processing, 2010, 58(3): 1553-1564.
［8］TANG H F, CHEN J, DONG G M. Sparse representation based latent components analysis for machinery weak fault detection［J］.Mechanical Systems and Signal Processing, 2014, 46: 373-388.
［9］HE G L, DING K, LIN H B. Fault feature extraction of rolling element beari ngs using sparse representation［J］. Journal of Sound and Vibration, 2016, 366:514-527.
［10］FAN W, CAI G G, ZHU Z K, et al. Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction［J］. Mechanical Systems and Signal Processing, 2015, 56/57: 230-245.
［11］SUN R B, YANG Z B, ZHAI Z, et al. Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis［J］. Mechanical Systems and Signal Processing, 2019, 122:737-753.
［12］HE G L, DING K, LIN H B. Gearbox coupling modulation separation method based on match pursuit and correlation filtering［J］. Mechanical Systems and Signal Processing, 2016, 66/67: 597-611.
［13］ZI Y Y, CHEN X F, HE Z J, et al. Vibration based modal parameters identification and wear fault diagnosis using laplace wavelet［J］. Key Engineering Materials, 2005, 293/294: 183-192.［14］WANG S B, HUANG W G, ZHU Z K. Transient modeling and parameter identi fication based on wavelet and corre lation filtering for rotating machine fault diagnosis［J］. Mechanical Systems and Signal Processing, 2011, 25: 1299-1320.
［15］赵春华, 胡恒星, 陈保家,等. 基于深度学习特征提取和WOA-SVM状态识别的轴承故障诊断［J］ .振动与冲击, 2019, 38(10):31-37.
ZHAO Chunhua, HU Hengxing, CHEN Baojia, et al. Bearing fault diagnosis based on the deep learning feature extracti on and WOA-SVM state recognition［J］. Journal of Vibration and Shock, 2019, 38(10):31-37.
［16］MIＲJALILI S, MIＲJALILI S M, LEWIS A. The whale optimization algorithm［J］. Advances in Engineering Software, 2016, 95(5): 51-67．
［17］ZHANG X, LIU Z W, MIAO Q, et al. Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time-frequency atom dictionary［J］. Mechanical Systems and Signal Processing, 2018, 107: 29-42.
［18］詹长庚，万书亭，王聪. 风机传动齿轮箱故障的Hilbert及倒谱综合分析［J］. 电力科学与工程, 2013, 29(5):1-7.
ZHAN Changgeng, WAN Shuting, WANG Cong. Comprehensive analysis of fan drive gear box based on hilbert demodulation and cepstrum［J］. Electric Power Engineering, 2013, 29(5):1-7.
［19］蔡剑华, 王先春, 胡惟文. 基于经验模态分解与小波阈值的MT信号去噪方法［J］. 石油地球物理勘探, 2013, 48(2):303-307.
CAI Jianhua, WANG Xianchun, HU Weiwen. A method for MT data denoising based on empirical mode decomposition and wavelet threshold［J］. Oil Geophysical Prospecting, 2013, 48(2):303-307.
［20］ZHAO C, FENG Z P, WEI X K, et al. Sparse classification based on dictionary learning for planet bearing fault identification［J］. Expert Systems with Applications, 2018, 108: 233-245.
