Fault Diagnosis Method for Rotating Machinery Based on Intrinsic Component Filtering

被引：0

作者：

Zhang Z. ^{[1
,2
]}

Han B. ^{[1
]}

Li S. ^{[2
]}

Bao H. ^{[1
]}

Wang J. ^{[1
]}

机构：

[1] College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao

[2] College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing

来源：

Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2024年 / 44卷 / 01期

关键词：

compound fault separation; fault diagnosis; intrinsic component filtering; rotating machinery; unsupervised learning; weak signal detection;

D O I：

10.16450/j.cnki.issn.1004-6801.2024.01.024

中图分类号：

学科分类号：

摘要：

Aiming at the challenge of weak compound fault diagnosis of rotating machinery，a novel method named intrinsic component filtering（ICF）is proposed for signature detection and separation under noisy environments norms of the rows and norms of the columns are used to achieve the sparse distribution in each sample and consistency among samples，respectively. Optimum filters are learned through minimizing the objective function. First，Hankel training matrix of the input signal is constructed，and the convolution process is simulated by the product of the weight matrix and Hankel matrix. Then，ICF is used to learn the feature matrix. The final optimum filters are determined through the Kurtosis of the trained filters. Finally，we can diagnose the fault condition using the extracted features and the corresponding envelope spectral. The simulated and experimental fault data are used to validate the performance of the proposed method. The results confirm that the proposed method can separate the weak fault components and guarantee strong robustness under strong noisy environment without any prior experience. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.

引用

页码：159 / 165

页数：6

共 13 条

[1] JIANG X X, WANG J, Et al., A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines［J］, Mechanical Systems and Signal Processing, 116, pp. 668-692, (2019)
[2] ZHANG Xuhui, ZHANG Chao, FAN Hongwei, Et al., Improved fault diagnosis of rolling bearing by fast kurtogram and order analysis［J］, Journal of Vibration，Measurement & Diagnosis, 41, 6, pp. 1090-1095, (2021)
[3] WANG J R, Et al., Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines［J］, Neurocomputing, 329, pp. 53-65, (2019)
[4] WANG J R, JIANG X X, Et al., An automatic feature extraction method and its application in fault diagnosis［J］, Journal of Vibroengineering, 19, 4, pp. 2521-2533, (2017)
[5] CHEN Z H，, Et al., Sparse filtering［C］∥Proceedings of the 24th International Conference on Neural Information Processing Systems, pp. 1125-1133, (2011)
[6] Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery［J］, Mechanical Systems and Signal Processing, 102, pp. 198-213, (2018)
[7] ZHANG Z Z, WANG J R，, Et al., General normalized sparse filtering： a novel unsupervised learning method for rotating machinery fault diagnosis ［J］, Mechanical Systems and Signal Processing, 124, pp. 596-612, (2019)
[8] KANG Wei, ZHU Yongsheng, YAN Ke, Et al., Weak fault extraction of rolling element bearings based on CSES and MED［J］, Journal of Vibration，Measurement & Diagnosis, 41, 4, pp. 660-666, (2021)
[9] WIGGINS R A., Minimum entropy deconvolution［J］, Geoexploration, 16, 1/2, pp. 21-35, (1978)
[10] WANG Zhijian, WANG Junyuan, ZHANG Jiping, Et al., Fault diagnosis of gearbox based on improved MOMEDA［J］, Journal of Vibration，Measurement & Diagnosis, 38, 1, pp. 176-181, (2018)

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