Fault feature extraction method of rolling bearings based on ITD-SCS

Yu Jianbo1 Liu Haiqiang1 Zheng Xiaoyun1 Zhou Binhai1 Cheng Hui2 Sun Xiwu2

Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (19) : 23-29.

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PDF(944 KB)
Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (19) : 23-29.

Fault feature extraction method of rolling bearings based on ITD-SCS

  • Yu Jianbo1  Liu Haiqiang1  Zheng Xiaoyun1  Zhou Binhai1  Cheng Hui2  Sun Xiwu2
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Abstract

Aiming at problems of early faults of rolling bearings having the feature of periodic impacts and very difficult to extract due to being submerged by strong noise, a new method for fault feature extraction of rolling bearings based on the time scale decomposition (ITD) and the sparse coding shrinkage (SCS) or ITD-SCS was proposed here.ITD could be used to adaptively decompose non-stationary and nonlinear vibration signals into several intrinsic rotation components or proper rotations (PRs), some of them were effectively selected to highlight impact features of original signals.Furthermore, the singular value decomposition (SVD) was used to perform noise-filtering for each effective PR, and SVD was taken as the pre-filtering noise unit of SCS to improve signals’ sparsity.Finally, SCS used the maximum likelihood estimation to extract impact features in synthetic signals.Numerical simulation results and testing ones for rolling bearings’ fault vibration signals showed that ITD-SCS method can be used to effectively extract impact features of bearing fault signals under strong background noise.

Key words

 Bearing fault / Fault feature extraction / Intrinsic time scale decomposition / Singular value decomposition / Sparse coding shrinkage

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Yu Jianbo1 Liu Haiqiang1 Zheng Xiaoyun1 Zhou Binhai1 Cheng Hui2 Sun Xiwu2. Fault feature extraction method of rolling bearings based on ITD-SCS[J]. Journal of Vibration and Shock, 2018, 37(19): 23-29

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