scholarly journals Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangdong Huang ◽  
Xukang Jin ◽  
Haipeng Fu
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Low Complexity ◽  
Fast Response ◽  
Rotating Machinery ◽  
Matrix Estimation ◽  
Application Fields ◽  
Spectrum Correction ◽  
Machinery Fault Diagnosis

Nowadays, the existing blind source separation (BSS) algorithms in rotating machinery fault diagnosis can hardly meet the demand of fast response, high stability, and low complexity simultaneously. Therefore, this paper proposes a spectrum correction based BSS algorithm. Through the incorporation of FFT, spectrum correction, a screen procedure (consisting of frequency merging, candidate pattern selection, and single-source-component recognition), modifiedk-means based source number estimation, and mixing matrix estimation, the proposed BSS algorithm can accurately achieve harmonics sensing on field rotating machinery faults in case of short-sampled observations. Both numerical simulation and practical experiment verify the proposed BSS algorithm’s superiority in the recovery quality, stability to insufficient samples, and efficiency over the existing ICA-based methods. Besides rotating machinery fault diagnosis, the proposed BSS algorithm also possesses a vast potential in other harmonics-related application fields.

scholarly journals A Sparse Underdetermined Blind Source Separation Method and Its Application in Fault Diagnosis of Rotating Machinery

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
HongChao Wang ◽  
WenLiao Du
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Underdetermined Blind Source Separation ◽  
Rolling Element ◽  
Element Bearing ◽  
Non Gaussian

Rolling element bearing is one of the most commonly used supporting parts in rotating machinery, and it is also one of the most easily failing rotating parts. It is of great safety and economic significance to study the effective fault diagnosis method of rolling element bearing. The fault characteristic signal of rolling bearing is often affected by other interference signals in practical engineering, and the situation is much more serious when the rolling bearing fault occurs in gearbox. Besides, only a limited number of measuring points are used in the process of rolling bearing fault signal acquisition due to the limitation of sensors installation condition. In some sense, the above two factors often cause the result that the fault diagnosis of rolling bearing is the problem of underdetermined blind source separation. The independence and non-Gaussian characteristic of the observed signals are the prerequisite of most of existent blind source separation methods. Unlike traditional blind source separation methods, SCA originating from sparse representation is an effective method to solve the problem of underdetermined blind source separation, because it does not require the independence or non-Gaussian characteristics of the observed signals, and it only makes full use of the sparse characteristics of the observed signals to extract the source signal from the observed signals. Based on these, a sparse component analysis (SCA) method based on linear clustering (LC) named LC-SCA is proposed for the purpose of underdetermined blind source separation of vibration signals of rolling element bearing, and the LC is introduced into SCA to improve the computation efficiency of SCA. The effectiveness of the proposed method is verified by simulation and experiment. In addition, the superiority of the method is verified by comparison with the other related methods such as constrained independent component analysis (cICA) and SCA.

Digital Twin for rotating machinery fault diagnosis in smart manufacturing

2018 ◽  
Vol 57 (12) ◽  
pp. 3920-3934 ◽  
Author(s):  
Jinjiang Wang ◽  
Lunkuan Ye ◽  
Robert X. Gao ◽  
Chen Li ◽  
Laibin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Machinery Fault Diagnosis
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