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

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.

Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN

“…To simulate the failure of the bearing under operating conditions in order to find the best method for extracting the bearing vibration signal features, the sampling frequency was set to 8192 Hz, and the number of sampling points was set to 4096. y = y 0 e −2πgf n t 0 sin πf n (1 − g 2 (t 0 − KT) (21) x =(1 + cos(2πf r t)) cos(2πf z t) (22) where y 0 is the displacement constant, set to 5; g is the damping coefficient, set to 0.5; f n is the intrinsic frequency, set to 1000 Hz; t 0 is the single-cycle sampling interval; K is the number of repetitions of the shock movement; T is the repetition period, set to 0.025 s; f r is the amplitude modulation frequency, set to 70 Hz; f z is the carrier frequency, set to 560 Hz. The time and frequency domain diagrams of the shock movements are shown in Figures 14 and 15 The sparse decomposition high-resonance quality factor, Q 1 , was set to 3 dancy, r 1 , was set to 3; the number of decomposition layers, J 1 , was set to 27; resonance quality factor, Q 2 , and the redundancy, r 2 , were set to 3; the number o position layers, J 2 , was set to 7 [34]. The high-resonance component retrieved composition and the moderate-resonance components are depicted in Figures 18 respectively.…”

Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

“…These researches all employed the original RSSD technique, where the determination of the decomposition parameters is quite arbitrary, relying mostly on prior information. According to some references [28,29], the selection of the Q-factors plays a crucial part in the performance of RSSD. Therefore, quite a few researchers have paid attention to optimizing the decomposition parameters.…”

A new method of vibration signal denoising based on improved wavelet

“…When wavelet transform is applied to denoise, the key is to set appropriate threshold. [9][10][11] According to different threshold setting strategies, it can be roughly divided into soft threshold method and hard threshold method. MATLAB provides corresponding implementation functions for both wavelet denoising methods.…”