Blind source separation of composite bearing vibration signals with low-rank and sparse decomposition

Measurement ◽  
2019 ◽  
Vol 145 ◽  
pp. 323-334 ◽  
Author(s):  
Guozheng Li ◽  
Gang Tang ◽  
Huaqing Wang ◽  
Yanan Wang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Low Rank ◽  
Vibration Signals ◽  
Sparse Decomposition ◽  
Bearing Vibration ◽  
Composite Bearing

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

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.

scholarly journals Generalized independent low-rank matrix analysis using heavy-tailed distributions for blind source separation

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Daichi Kitamura ◽  
Shinichi Mogami ◽  
Yoshiki Mitsui ◽  
Norihiro Takamune ◽  
Hiroshi Saruwatari ◽  
...  
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Matrix Analysis ◽  
Low Rank ◽  
Heavy Tailed Distributions ◽  
Rank Matrix ◽  
Heavy Tailed ◽  
Low Rank Matrix

Research on Identification Method of Multi-Mixed Vibration Signal of Aeroengine

2009 ◽  
Vol 419-420 ◽  
pp. 801-804
Author(s):  
Xiang Yang Jin ◽  
Shi Sheng Zhong
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signal ◽  
Signal Separation ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Vibration Data ◽  
Monitoring And Diagnosis ◽  
Rotating Shafts

The effectiveness of separation and identification of mechanical signals vibrations is crucial to successful fault diagnosis in the condition monitoring and diagnosis of complex machines.Aeroengine vibration signals always include many complicated components, blind source separation (BSS) provides a efficient way to separate the independent component.In order to get the most effective algorithm of vibration signal separation,experiment has been done to acquire plenty of multi-mixed rotor vibration signals,three sets of vibration data generated from aeroengine rotating shafts were separated from the synthetic vibration signal. The results prove that blind source separation is effective and can be applied for vibration signal processing and fault diagnosis of aeroengine.

scholarly journals A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1713 ◽  
Author(s):  
Feng Miao ◽  
Rongzhen Zhao ◽  
Xianli Wang ◽  
Leilei Jia
Keyword(s):  
Blind Source Separation ◽  
Median Filter ◽  
Source Separation ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Vibration Signals ◽  
Pulse Noise ◽  
Feature Extraction Method ◽  
Multiple Sensors ◽  
Signal Processing Methods

During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments.

Fast and unique Tucker decompositions via multiway blind source separation

2012 ◽  
Vol 60 (3) ◽  
pp. 389-405 ◽  
Author(s):  
G. Zhou ◽  
A. Cichocki
Keyword(s):  
Dimensionality Reduction ◽  
Blind Source Separation ◽  
Large Scale ◽  
High Efficiency ◽  
Source Separation ◽  
Low Rank ◽  
Large Scale Problems ◽  
Rank Matrix ◽  
Reduction Methods ◽  
Low Rank Matrix

Abstract A multiway blind source separation (MBSS) method is developed to decompose large-scale tensor (multiway array) data. Benefitting from all kinds of well-established constrained low-rank matrix factorization methods, MBSS is quite flexible and able to extract unique and interpretable components with physical meaning. The multilinear structure of Tucker and the essential uniqueness of BSS methods allow MBSS to estimate each component matrix separately from an unfolding matrix in each mode. Consequently, alternating least squares (ALS) iterations, which are considered as the workhorse for tensor decompositions, can be avoided and various robust and efficient dimensionality reduction methods can be easily incorporated to pre-process the data, which makes MBSS extremely fast, especially for large-scale problems. Identification and uniqueness conditions are also discussed. Two practical issues dimensionality reduction and estimation of number of components are also addressed based on sparse and random fibers sampling. Extensive simulations confirmed the validity, flexibility, and high efficiency of the proposed method. We also demonstrated by simulations that the MBSS approach can successfully extract desired components while most existing algorithms may fail for ill-conditioned and large-scale problems.

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