Please wait a minute...
浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (5): 1018-1030    DOI: 10.3785/j.issn.1008-973X.2025.05.015
    
Adaptive sparse representation atom construction method for gearbox diagnosis
Changqing ZHOU1,2(),Yaochun HOU2,Peng WU2,*(),Shuai YANG2,Dazhuan WU2
1. Shanghai Marine Equipment Research Institute, Shanghai 200031, China
2. College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
Download: HTML     PDF(4602KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

An improved atom pursuit method based on the asymmetric Gaussian chirplet model (AGCM) was developed to achieve better performance in low signal-to-noise ratio environment in order to address the issue of poor optimal atom pursuit performance in traditional sparse representation algorithms under strong interference conditions in gearbox. Wavelet atoms were constructed by using AGCM with multi-parameter and variable shapes. Then a feature-enhanced signal was obtained through an impulse enhancement method. Atom parameters most compatible with fault impulses were identified by maximizing a statistical index under Gaussian hypothesis. Then fault transient components were separated through a multiple enhancement sparse representation algorithm. The effectiveness of the proposed method was validated by using both public dataset and gearbox fault simulation dataset. Comparative analysis with original methods and other existing approaches demonstrates that the proposed method can construct superior sparse representation atoms under strong interference conditions in gearbox signals.

Key words： gearbox      fault diagnosis      sparse representation      asymmetric Gaussian chirplet model      impulse enhancement method     
Received: 29 March 2024      Published: 25 April 2025
CLC:  TH 311  
Fund:  核技术研发科研资助项目.
Corresponding Authors: Peng WU     E-mail: cqzhou@zju.edu.cn;roc@zju.edu.cn
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Changqing ZHOU
Yaochun HOU
Peng WU
Shuai YANG
Dazhuan WU
Cite this article:

Changqing ZHOU,Yaochun HOU,Peng WU,Shuai YANG,Dazhuan WU. Adaptive sparse representation atom construction method for gearbox diagnosis. Journal of ZheJiang University (Engineering Science), 2025, 59(5): 1018-1030.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.05.015     OR     https://www.zjujournals.com/eng/Y2025/V59/I5/1018


自适应齿轮箱稀疏表示原子构建方法

针对传统稀疏表示算法在齿轮箱信号干扰较多的情况下最优原子寻优效果不佳的问题，开发基于非对称高斯啁啾模型的改进原子寻优方法，以实现在低信噪比环境中获得更佳性能的目标. 利用具有多参数形状多变的非对称高斯啁啾模型，构建小波原子. 利用构建的小波原子，通过脉冲特征增强方法得到原始振动信号的特征增强信号. 通过最大化高斯条件下的循环平稳性检验指标，寻找与故障脉冲最匹配的小波原子参数，通过多重增强稀疏表示算法分离出故障瞬态分量. 通过公开数据集与故障模拟实验中齿轮箱故障数据集，验证了本文方法的有效性，并与原始方法和其他方法进行对比，证明了本文方法能够在齿轮箱信号存在较多干扰的情况下构建较优的稀疏表示原子.


关键词： 齿轮箱,  故障诊断,  稀疏表示,  非对称高斯啁啾模型,  脉冲特征增强 
Fig.1 Diagram of impulse response square wave
Fig.2 Schematic diagram of optimal atomic construction of MESDOA method
Fig.3 Gearbox fault simulation test bench
Fig.4 Gearbox structure diagram of gearbox fault simulation test bench
Fig.5 Diagram of faulty component of helical gear in gearbox
Fig.6 Gearbox rolling bearing fault component diagram
Fig.7 PHM 2009 data set experimental bench and gearbox internal physical diagram
Fig.8 Structure diagram of PHM 2009 data set gearbox
Fig.9 AGCM atom optimization diagram of spur 7 data of PHM 2009 data set’s rolling bearing component
Fig.10 Sparse representation result of PHM 2009 spur 7 dataset
Fig.11 Sparse representation result of PHM 2009 helical 5 dataset
Fig.12 Sparse representation result of rolling bearing fault in gearbox fault simulation data set
Fig.13 Sparse representation result of gear fault in gearbox fault simulation dataset
Fig.14 Radar chart of rolling bearing inner race fault characteristics extracted by various methods of PHM 2009 helical 5 data
Fig.15 Radar chart of gear fault characteristics extracted by various methods of PHM 2009 helical 5 data
Fig.16 Radar chart of rolling bearing inner race fault characteristics extracted by various methods of gearbox simulation data set
Fig.17 Radar chart of gear fault characteristics extracted by various methods of gearbox simulation data set
Fig.18 Sparse representation result of PHM 2009 helical 5 data with kurtosis and generalized likelihood ratio index
[1]   HOU Y, WU P, WU D An operating condition information-guided iterative variational mode decomposition method based on Mahalanobis distance criterion for surge characteristic frequency extraction of the centrifugal compressor[J]. Mechanical Systems and Signal Processing, 2023, 186 (3): 109836
[2]   HOU Y, ZHOU C, TIAN C, et al Acoustic feature enhancement in rolling bearing fault diagnosis using sparsity-oriented multipoint optimal minimum entropy deconvolution adjusted method[J]. Applied Acoustics, 2022, 201 (12): 109105
[3]   侯耀春, 周昶清, 武鹏, 等. 基于最大李雅普诺夫指数异常感知和CatBoost识别的机械密封失效模式层次化诊断框架[J]. 工程热物理学报, 2024, 45(1): 93-100.
HOU Yaochun, ZHOU Changqing, WU Peng, et al. Hierarchical diagnostic framework of mechanical seal failure modes based on maximum Lyapunov exponent anomaly sensing and CatBoost model recognition [J]. Journal of Engineering Thermophysics . 2024, 45(1): 93-100.
[4]   丁康, 李巍华, 朱小勇. 齿轮及齿轮箱故障诊断实用技术[M]. 北京: 机械工业出版社, 2005.
[5]   ERRICHELLO R, MULLER J. Gearbox reliability collaborative gearbox 1 failure analysis report: December 2010-January 2011 [R]. United States: National Renewable Energy Lab, 2012.
[6]   WANG W Q, ISMAIL F, GOLNARAGHI M F Assessment of gear damage monitoring techniques using vibration measurements[J]. Mechanical Systems and Signal Processing, 2001, 15 (5): 905- 922
doi: 10.1006/mssp.2001.1392
[7]   HUANG W, LI S, FU X, et al. Transient extraction based on minimax concave regularized sparse representation for gear fault diagnosis [J]. Measurement . 2020, 151(2): 107273.
[8]   WANG S, HUANG W, ZHU Z K Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis[J]. Mechanical Systems and Signal Processing, 2011, 25 (4): 1299- 1320
doi: 10.1016/j.ymssp.2010.10.013
[9]   LI N, HUANG W, GUO W, et al Multiple enhanced sparse decomposition for gearbox compound fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69 (3): 770- 781
[10]   WANG S, SELESNICK I, CAI G, et al Nonconvex sparse regularization and convex optimization for bearing fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2018, 65 (9): 7332- 7342
doi: 10.1109/TIE.2018.2793271
[11]   ZHOU Q, ZHANG Y, YI C, et al Convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model in bearing fault detection[J]. IEEE Sensors Journal, 2021, 21 (16): 18132- 18145
doi: 10.1109/JSEN.2021.3086015
[12]   SELESNICK I Sparse regularization via convex analysis[J]. IEEE Transactions on Signal Processing, 2017, 65 (17): 4481- 4494
doi: 10.1109/TSP.2017.2711501
[13]   RAGUET H, LANDRIEU L Preconditioning of a generalized forward-backward splitting and application to optimization on graphs[J]. SIAM Journal on Imaging Sciences, 2015, 8 (4): 2706- 2739
doi: 10.1137/15M1018253
[14]   ANTONI J, BORGHESANI P A statistical methodology for the design of condition indicators[J]. Mechanical Systems and Signal Processing, 2019, 114 (1): 290- 327
[15]   EDWARDS A W F. Likelihood [M]// Time series and statistics . New York: Springer, 1972: 126-129.
[16]   RICE J A. Mathematical statistics and data analysis [M]. Stamford: Cengage Learning, 2006.
[17]   LIN H, WU F, HE G Rolling bearing fault diagnosis using impulse feature enhancement and nonconvex regularization[J]. Mechanical Systems and Signal Processing, 2020, 142 (8): 106790
[18]   PHM Society. Public Data Sets. 2009 PHM challenge competition data set [DB/OL]. (2009-08-10)[2024-05-21]. https://phmsociety.org/public-data-sets.
[19]   HOU B, WANG D, KONG J, et al Understanding importance of positive and negative signs of optimized weights used in the sum of weighted normalized Fourier spectrum/envelope spectrum for machine condition monitoring[J]. Mechanical Systems and Signal Processing, 2022, 174 (7): 109094
[20]   MCDONALD G L, ZHAO Q Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection[J]. Mechanical Systems and Signal Processing, 2017, 82 (1): 461- 477
[21]   LOPEZ C, WANG D, NARANJO A, et al Box-cox-sparse-measures-based blind filtering: understanding the difference between the maximum kurtosis deconvolution and the minimum entropy deconvolution[J]. Mechanical Systems and Signal Processing, 2022, 165 (2): 108376
[1] Xianwei MA,Chaohui FAN,Weizhi NIE,Dong LI,Yiqun ZHU. Robust fault diagnosis method for failure sensors[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(7): 1488-1497.
[2] Kang HAN,Hongfei ZHAN,Junhe YU,Rui WANG. Rolling bearing fault diagnosis based on dilated convolution and enhanced multi-scale feature adaptive fusion[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(6): 1285-1295.
[3] Shuai SHAO,Kailin ZHANG,Yuan YAO,Yi LIU,Zhengyang WANG. Splash lubrication characteristics and structure improvement of spiral bevel gearbox for electrical multiple unit[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(5): 1060-1071.
[4] Zhiqiang GENG,Wei CHEN,Bo MA,Yongming HAN. Bearing intelligent fault diagnosis method based on continuous wavelet convolutional neural network[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(10): 2069-2075.
[5] Yu-xiang WANG,Zhi-wei ZHONG,Peng-cheng XIA,Yi-xiang HUANG,Cheng-liang LIU. Compound fault decoupling diagnosis method based on improved Transformer[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 855-864.
[6] Chao-qi WU,Jian LUO,Ying ZHOU,Peng LOU,Yu YU. Performance simulation of complex oil circuit of inlet gearbox[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(11): 2337-2344.
[7] Wei SUN,Heng LIU,Jian-feng TAO,Hao SUN,Cheng-liang LIU. IndRNN-1DLCNN based fault diagnosis of independent metering valve-controlled hydraulic cylinder system[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(10): 2028-2041.
[8] Fei SUN,Xiao-run LI,Liao-ying ZHAO,Shao-qi YU. Anomaly detection algorithm based on FrFT transform and total variation regularization[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(7): 1276-1284.
[9] Guo-hua ZHOU,Jian-wei LU,Tong-guang NI,Xue-long HU. Hierarchical nonlinear subspace dictionary learning[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1159-1167.
[10] Zhu-peng WEN,Jie CHEN,Lian-hua LIU,Ling-ling JIAO. Fault diagnosis of wind power gearbox based on wavelet transform and improved CNN[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1212-1219.
[11] Jin-sheng JIANG,Hao-ran REN,Han-ye LI. Seismic data processing based on convolutional autoencoder[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(5): 978-984.
[12] Le XIE,Xi-dan HENG,Yang LIU,Qi-long JIANG,Dong LIU. Transformer fault diagnosis based on linear discriminant analysis and step-by-step machine learning[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(11): 2266-2272.
[13] Yu ZHANG,Kai-lin ZHANG,Yuan YAO. Impact of rotating and thermal effects on leakage performance of gearbox with axial labyrinth seal[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(9): 1656-1662.
[14] Jing-jing LIN,Yan-xia SHEN. Stator current sensors’ fault tolerant control for permanent magnet synchronous motor drive system[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(9): 1815-1825.
[15] Zheng ZHANG,Xue-jun ZHOU,Xi-chen WANG,Yuan-yuan ZHOU. Short-circuit fault diagnosis and interval location method for constant current remote supply system in cabled underwater information networks[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1190-1197.