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Sensors 2017, 17(6), 1279; doi:10.3390/s17061279

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review

School of Mechatronics Engineering, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, China
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Author to whom correspondence should be addressed.
Academic Editor: Xue Wang
Received: 7 April 2017 / Revised: 27 May 2017 / Accepted: 1 June 2017 / Published: 3 June 2017
(This article belongs to the Section Physical Sensors)

Abstract

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis. View Full-Text
Keywords: resonance-based sparse signal decomposition; signal processing; mechanical fault diagnosis; feature extraction resonance-based sparse signal decomposition; signal processing; mechanical fault diagnosis; feature extraction
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Huang, W.; Sun, H.; Wang, W. Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review. Sensors 2017, 17, 1279.

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