scholarly journals Blind Source Separation Method for Bearing Vibration Signals

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 658-664 ◽  
Author(s):  
He Jun ◽  
Yong Chen ◽  
Qing-Hua Zhang ◽  
Guoxi Sun ◽  
Qin Hu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Separation Method ◽  
Vibration Signals ◽  
Bearing Vibration

Blind Separation Method for Gearbox Mixed Fault Signals

2011 ◽  
Vol 86 ◽  
pp. 180-183
Author(s):  
Yan Bin Lei ◽  
Zhi Gang Chen ◽  
Hai Ou Liu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Higher Order ◽  
Separation Method ◽  
New Method ◽  
Blind Separation ◽  
Vibration Signals ◽  
Mechanical Simulation ◽  
Gearbox Vibration

A new blind source separation (BSS) algorithm used for separating mixed gearbox signals is proposed in this paper. Firstly, whiten the observed signals, and then diagonalize the second- and higher-order cumulant matrix to get an orthogonal separation matrix. The feasibility of the algorithm is validated through separating the mechanical simulation signals and the gearbox vibration signals. The algorithm can successfully identified the failure source of the gearbox and provides a new method to a gearbox fault.

scholarly journals Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis

2019 ◽  
Vol 9 (9) ◽  
pp. 1852 ◽  
Author(s):  
Hua Ding ◽  
Yiliang Wang ◽  
Zhaojian Yang ◽  
Olivia Pfeiffer
Keyword(s):  
Blind Source Separation ◽  
Additive Noise ◽  
Rbf Neural Network ◽  
Source Separation ◽  
Mining Machine ◽  
Vibration Source ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Strongly Nonlinear ◽  
Nonlinear Mixtures

Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind source separation (BSS) algorithm to separate the critical independent sources associated with the transmission fault vibrations. For this reason, a new method based on wavelet de-noising and nonlinear independent component analysis (ICA) is presented in this paper to tackle the nonlinear BSS problem with additive noise. The wavelet de-noising approach was first employed to eliminate the influence of the additive noise in the BSS procedure. Then, the radial basis function (RBF) neural network combined with the linear ICA was applied to the de-noised vibration signals. Vibration sources involved with the machine faults were separated. Subsequently, wavelet package decomposition (WPD) was used to extract distinct fault features from the source signals. Lastly, an RBF classifier was used to recognize the fault patterns. Field data acquired from a mining machine was used to evaluate and validate the proposed diagnostic method. The experimental analysis results show that critical fault vibration source component can be separated by the proposed method, and the fault detection rate is superior to the linear ICA based approaches.

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