Single Channel Blind Source Separation Based on Dual-Tree Complex Wavelet Transform And Ensemble Empirical Mode Decomposition

2019 ◽  
Author(s):  
Shexiang Ma ◽  
Liuyi Yang ◽  
Xin Meng
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Ensemble Empirical Mode Decomposition ◽  
Complex Wavelet Transform ◽  
Mode Decomposition ◽  
Complex Wavelet

scholarly journals Gearbox Composite Fault Diagnosis Method Based on Minimum Entropy Deconvolution and Improved Dual-Tree Complex Wavelet Transform

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 18 ◽  
Author(s):  
Ziying Zhang ◽  
Xi Zhang ◽  
Panpan Zhang ◽  
Fengbiao Wu ◽  
Xuehui Li
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Notch Filter ◽  
Vibration Signal ◽  
Coefficient Matrix ◽  
Ensemble Empirical Mode Decomposition ◽  
Minimum Entropy ◽  
Complex Wavelet Transform ◽  
Mode Decomposition ◽  
Complex Wavelet

Dual-tree complex wavelet transform has been successfully applied to the composite diagnosis of a gearbox and has achieved good results. However, it has some fatal weaknesses, so this paper proposes an improved dual-tree complex wavelet transform (IDTCWT), and combines minimum entropy deconvolution (MED) to diagnose the composite fault of a gearbox. Firstly, the number of decomposition levels and the effective sub-bands of the DTCWT are adaptively determined according to the correlation coefficient matrix. Secondly, frequency mixing is removed by notch filter. Thirdly, each of the obtained sub-bands further reduces the noise by minimum entropy deconvolution. Then, the proposed method and the existing adaptive noise reduction methods, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the two sets of simulation signals in comparison, and the feasibility of the proposed method has been verified. Finally, the proposed method is applied to the compound fault vibration signal of a gearbox. The results show the proposed method successfully extracts the outer ring fault at a frequency of 160 Hz, the gearbox fault with a characteristic frequency of 360 Hz and its double frequency of 720 Hz, and that there is no mode mixing. The method proposed in this paper provides a new idea for the feature extraction of a gearbox compound fault.

Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition

2015 ◽  
Vol 230 (2) ◽  
pp. 291-302 ◽  
Author(s):  
Mien Van ◽  
Hee-Jun Kang
Keyword(s):  
Feature Selection ◽  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Complex Wavelet Transform ◽  
Two Stage ◽  
Mode Decomposition ◽  
Complex Wavelet ◽  
Neighbor Classifier

This paper presents an automatic fault diagnosis of different rolling element bearing faults using a dual-tree complex wavelet transform, empirical mode decomposition, and a novel two-stage feature selection technique. In this method, dual-tree complex wavelet transform and empirical mode decomposition were used to preprocess the original vibration signal to obtain more accurate fault characteristic information. Then, features in the time domain were extracted from each of the original signals, the coefficients of the dual-tree complex wavelet transform, and some useful intrinsic mode functions to generate a rich combined feature set. Next, a two-stage feature selection algorithm was proposed to generate the smallest set of features that leads to the superior classification accuracy. In the first stage of the two-stage feature selection, we found the candidate feature set using the distance evaluation technique and a k-nearest neighbor classifier. In the second stage, a genetic algorithm-based k-nearest neighbor classifier was designed to obtain the superior combination of features from the candidate feature set with respect to the classification accuracy and number of feature inputs. Finally, the selected features were used as the input to a k-nearest neighbor classifier to evaluate the system diagnosis performance. The experimental results obtained from real bearing vibration signals demonstrated that the method combining dual-tree complex wavelet transform, empirical mode decomposition, and the two-stage feature selection technique is effective in both feature extraction and feature selection, which also increase classification accuracy.

Vibration Signal Processing for Gear Fault Diagnosis Based on Empirical Mode Decomposition and Nonlinear Blind Source Separation

2011 ◽  
Vol 42 (10) ◽  
pp. 55-61 ◽  
Author(s):  
Yu Jiang ◽  
Li Qin ◽  
Yuelei Zhang ◽  
Jingping Wu
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Vibration Signal ◽  
Normal Operation ◽  
Mode Decomposition ◽  
Gear Fault

Gear failures happen frequently in the gear mechanisms, and an unexpected serious gear fault may cause severe damage on the machinery. Hence, precise gear fault detection at the early stage is imperative to ensure the normal operation of the machinery. Independent component analysis (ICA) has been paid more and more attention for its powerful ability of separating the useful vibration source from the multi-sensor observations to enhance the fault feature extraction. This is the so called blind source separation (BSS) procedure. However, the popular ICA model may suffer from two limitations. One is the linear mixture assumption, and the other is the lack of sensor channels. Up to now, only limited research considered the nonlinear ICA model in the field of mechanic fault diagnosis, and techniques for the situation where the number of sensor channels is less than the number of independent sources for gear defect detection are scarce. In order to extract the useful source involved with the gear fault characteristics in single-channel vibration signal processing, this work presents a new method based on the empirical mode decomposition (EMD) and nonlinear ICA. The EMD was firstly employed to decompose the vibration signal into a number of intrinsic mode functions (IMFs), and then these IMFs were taken as the multi-channel observations. The post-nonlinear (PNL) ICA model based on the radial basis function (RBF) neural network was applied to the nonlinear BSS procedure on the IMFs. The experimental vibration data acquired from the gear fault test-bed were processed for the validation of the proposed method. The nonlinear ICA method has been compared with the linear ICA and non-ICA based approaches. The analysis results show that the sensitive characteristics of the gear meshing vibration can be separated from the single channel measurement by the proposed method, and the fault diagnosis precision can be enhanced significantly. The detection rate can be increased by 3.75% or better when the ICA based preprocessing is carried out, and the proposed nonlinear ICA outperforms the linear ICA detection model.

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