scholarly journals Sparse feature extraction for fault diagnosis of rotating machinery based on sparse decomposition combined multiresolution generalized S transform

2019 ◽  
Vol 38 (2) ◽  
pp. 441-456 ◽  
Author(s):  
Baokang Yan ◽  
Bin Wang ◽  
Fengxing Zhou ◽  
Weigang Li ◽  
Bo Xu
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Matching Pursuit ◽  
Maximum Energy ◽  
Rotating Machinery ◽  
Orthogonal Matching Pursuit ◽  
Sparse Decomposition ◽  
Feature Extraction Method ◽  
S Transform ◽  
Generalized S Transform

In order to extract fault impulse feature of large-scale rotating machinery from strong background noise, a sparse feature extraction method based on sparse decomposition combined multiresolution generalized S transform is proposed in this paper. In this method, multiresolution generalized S transform is employed to find the optimal atom for every iteration, which firstly takes in to account the generalized S transform with discretized adjustment factors, then builds an atom corresponding to the maximum energy. The multiresolution generalized S transform has better accuracy compared to generalized S transform and faster searching speed compared to the orthogonal matching pursuit method in selecting the optimal atom. Then, the orthogonal matching pursuit method is used to decompose the signal into several optimal atoms. The proposed method is applied to analyze the simulated signal and vibration signals collected from experimental failure rolling bearings. The results prove that the proposed method has better performances such as high precision and fast decomposition speed than the traditional orthogonal matching pursuit method method and local mean decomposition method.

scholarly journals Survey of Marine Organisms Based on Passive Acoustic Technology

2020 ◽  
Vol 15 (5) ◽  
pp. 729-737
Author(s):  
Gong Chen ◽  
Lei Cai ◽  
Lv Zong ◽  
Yan Wang ◽  
Xin Yuan
Keyword(s):  
Feature Extraction ◽  
Acoustic Signal ◽  
Extraction Method ◽  
Acoustic Signals ◽  
Marine Organisms ◽  
Decomposition Algorithm ◽  
Sparse Decomposition ◽  
Feature Extraction Method ◽  
Fish Detection ◽  
Passive Acoustic

Passive acoustic technology (PAT) is an important tool to acquire the passive acoustic signals from marine organisms. In this paper, PAT fish detection is introduced at great length, including the relevant instruments, signal processing methods, and workflow. Focusing on the key tasks of PAT fish detection, the authors proposed a sparse decomposition algorithm that extracts coherent ratio of passive fish acoustic signal, and designed a feature extraction method for that signal based on speech imitation technology. Experimental results demonstrate that the proposed sparse decomposition algorithm can detect fish acoustic signal accurately at low signal-to-noise ratios (SNRs), and the proposed feature extraction method can effectively extract fish acoustic signals from the marine background. The research results shed important new light on the protection and management of fishery resources in the seas and oceans.

scholarly journals A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 231 ◽  
Author(s):  
Hanfei Zhang ◽  
Shungen Xiao ◽  
Ping Zhou
Keyword(s):  
Energy Distribution ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Selection Criterion ◽  
Critical Factor ◽  
Maximum Energy ◽  
Orthogonal Matching Pursuit ◽  
Support Set ◽  
Reconstruction Efficiency ◽  
Matching Pursuit Algorithm

The signal reconstruction quality has become a critical factor in compressed sensing at present. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting. This algorithm uses energy sorting for secondary atom screening to delete individual wrong atoms through the regularized orthogonal matching pursuit (ROMP) algorithm backtracking. The support set is continuously updated and expanded during each iteration. While the signal energy distribution is not uniform, or the energy distribution is in an extreme state, the reconstructive performance of the ROMP algorithm becomes unstable if the maximum energy is still taken as the selection criterion. The proposed method for the regularized orthogonal matching pursuit algorithm can be adopted to improve those drawbacks in signal reconstruction due to its high reconstruction efficiency. The experimental results show that the algorithm has a proper reconstruction.

A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine

2016 ◽  
Vol 231 (6) ◽  
pp. 1146-1161 ◽  
Author(s):  
Pak Kin Wong ◽  
Jian-Hua Zhong ◽  
Zhi-Xin Yang ◽  
Chi Man Vong
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Extraction Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Committee Machine ◽  
Feature Extraction Method ◽  
Diagnostic Framework ◽  
The Individual ◽  
New Framework

This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneous-fault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as different signal committee members, and each member is trained using vibration or sound signals respectively. The individual diagnostic result from each fault detection member is then combined by a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable fault as compared to individual classifier acting alone. The effectiveness of the proposed framework is verified by a case study on a gearbox fault detection. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single- and simultaneous-faults for the rotating machinery while the framework is trained by single-fault patterns only.

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