Endpoint Effect Suppression Based on Multipoint Extension in Bearing Fault Diagnosis

2019 ◽  
Vol 33 (09) ◽  
pp. 1950015 ◽  
Author(s):  
Shuang Xia ◽  
Guohui Zhou
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Rolling Bearing ◽  
Experimental Comparison ◽  
End Effect ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Data Distortion ◽  
Mode Decomposition

In the rolling bearing fault detection, the Hilbert–Huang transform (HHT) has made remarkable achievements, but at present, the HHT still has the end effect problem, which will cause a lot of data distortion, spectrum confusion that will affect fault diagnosis result and error in the detection of rolling bearing faults in a serious manner. In response to this problem, this paper proposes a method of multi-point continuation at both ends of the signal to suppress the endpoint effectExtend at both ends of the signal, then perform empirical mode decomposition (EMD). The experimental comparison shows that the method has an effect on the endpoint effect.

scholarly journals Rolling Bearing Fault Diagnosis under Variable Conditions Using Hilbert-Huang Transform and Singular Value Decomposition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hongmei Liu ◽  
Xuan Wang ◽  
Chen Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Singular Value ◽  
Instantaneous Amplitude ◽  
Elman Neural Network ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition

Fault diagnosis precision for rolling bearings under variable conditions has always been unsatisfactory. To solve this problem, a fault diagnosis method combining Hilbert-Huang transform (HHT), singular value decomposition (SVD), and Elman neural network is proposed in this paper. The method includes three steps. First, instantaneous amplitude matrices were obtained by using HHT from rolling bearing signals. Second, the singular value vector was acquired by applying SVD to the instantaneous amplitude matrices, thus reducing the dimension of the instantaneous amplitude matrix and obtaining the fault feature insensitive to working condition variation. Finally, an Elman neural network was applied to the rolling bearing fault diagnosis under variable working conditions according to the extracted feature vector. The experimental results show that the proposed method can effectively classify rolling bearing fault modes with high precision under different operating conditions. Moreover, the performance of the proposed HHT-SVD-Elman method has an advantage over that of EMD-SVD or WPT-PCA for feature extraction and Support Vector Machine (SVM) or Extreme Learning Machine (ELM) for classification.

A Research Review of Hilbert-Huang Transform Used for Rolling Bearing Fault Diagnosis

2013 ◽  
Vol 397-400 ◽  
pp. 2152-2155 ◽  
Author(s):  
Qi Li ◽  
Hui Wang
Keyword(s):  
Fault Diagnosis ◽  
Present Situation ◽  
Rolling Bearing ◽  
Research Direction ◽  
Research Review ◽  
Future Research ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Stationary Vibration

Non-stationary vibration will appear when the fault rolling bearing is running. This paper summarizes the development present situation of application of Hilbert-Huang Transform solving the problem of rolling bearing fault diagnosis at home and abroad from several aspects, analyzes the current rolling bearing fault diagnosis methods combined with HHT, and sums up the practical applicability of HHT method through the comparison of rolling bearing fault diagnosis with other methods. It points out that HHT used for rolling bearing fault diagnosis still has problems to be solved. At last, it gives the future research direction of HHT.

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