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

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.

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1713 ◽  
Author(s):  
Feng Miao ◽  
Rongzhen Zhao ◽  
Xianli Wang ◽  
Leilei Jia

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.


2019 ◽  
Vol 155 ◽  
pp. 63-72 ◽  
Author(s):  
Denis G. Fantinato ◽  
Leonardo T. Duarte ◽  
Yannick Deville ◽  
Romis Attux ◽  
Christian Jutten ◽  
...  

Author(s):  
Leonardo Tomazeli Duarte ◽  
Ricardo Suyama ◽  
Romis Ribeiro de Faissol Attux ◽  
Fernando José Von Zuben ◽  
João Marcos Travassos Romano

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 658-664 ◽  
Author(s):  
He Jun ◽  
Yong Chen ◽  
Qing-Hua Zhang ◽  
Guoxi Sun ◽  
Qin Hu

2009 ◽  
Vol 419-420 ◽  
pp. 801-804
Author(s):  
Xiang Yang Jin ◽  
Shi Sheng Zhong

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.


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