Multiple Window Decomposition of Time-Frequency Kernels Using A Penalty Function for Suppressed Sidelobes

2006 ◽  
Author(s):  
M. Hansson
Keyword(s):  
Penalty Function ◽  
Time Frequency ◽  
Multiple Window

scholarly journals Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Wentao He ◽  
Cancan Yi ◽  
Yourong Li ◽  
Han Xiao
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Total Variation ◽  
Penalty Function ◽  
Noise Signal ◽  
Mechanical Equipment ◽  
Denoising Method ◽  
Time Frequency ◽  
Local Variance ◽  
Total Variation Denoising

Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV) was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV) regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV) denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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