|Table of Contents|

A Machinery Fault Feature Extraction Approach IntegratingReweighted SVD with Periodic Overlapping Group Sparsity(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2018年04期
Page:
39-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
A Machinery Fault Feature Extraction Approach IntegratingReweighted SVD with Periodic Overlapping Group Sparsity
Author(s):
Ren Shijin1; Li Xinyu2; Xu Guiyun2; Pan Jianhan1; Yang Maoyun1
(1.School of Computer Science & Technology,Jiangsu Normal University,Xuzhou 221116,China)(2.School of Mechatronic Engineering,China University of Mining and Technology,Xuzhou 221116,China)
Keywords:
reweighted singular value decomposition(RSVD); periodic overlapping group sparsity(POGS); machinery fault diagnosis; sparse feature extraction; periodic modulation intensity(PMI)
PACS:
TP181
DOI:
10.3969/j.issn.1001-4616.2018.04.008
Abstract:
The machinery fault features are generally periodic,sparse and corrupted by heavy background noise. Most feature extraction methods like locally linear embedding(LLE),local tangent space alignment(LTSA)and etc.,are susceptible to noise. Therefore,signal denoising and faulty feature enhancement are critical for machinery fault detection. To address the issue,a reweighted singular value decomposition integrating with periodic overlapping group sparsity(RSVD-POGS)-based feature extraction approach is developed for machinery fault detection in this study. Firstly,RSVD is introduced to decompose multiple-component vibration signal into a set of singular components and periodic modulation intensity(PMI)criterion is utilized to select salient singular components. POGS is then conducted to enhance sparse and periodic impulse features hidden in the remained singular components,and the original signal can be constructed by the denoised singular components. The comparison between RSVD-POGS and POGS is carried out on low SNR simulated sparse periodic signal and RSVD-POGS is utilized to extract features rom normal and fault vibration signals collected from experimental platform. The experimental results demonstrate that the proposed method can effectively enhance the periodic and sparse fault features for machinery fault diagnosis.

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Last Update: 2018-12-30