Application of Wavelet Packet Sample Entropy in the Forecast of Rolling Element Bearing Fault Trend

2011 ◽  
Author(s):  
Fengtao Wang ◽  
Yangyang Zhang ◽  
Bin Zhang ◽  
Wensheng Su
Keyword(s):  
Wavelet Packet ◽  
Sample Entropy ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing

Rolling Element Bearing Fault Detection Using Redundant Second Generation Wavelet Packet Transform

2011 ◽  
Vol 199-200 ◽  
pp. 931-935 ◽  
Author(s):  
Ning Li ◽  
Rui Zhou
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Second Generation ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Second Generation Wavelet

Wavelet transform has been widely used for the vibration signal based rolling element bearing fault detection. However, the problem of aliasing inhering in discrete wavelet transform restricts its further application in this field. To overcome this deficiency, a novel fault detection method for roll element bearing using redundant second generation wavelet packet transform (RSGWPT) is proposed. Because of the absence of the downsampling and upsampling operations in the redundant wavelet transform, the aliasing in each subband signal is alleviated. Consequently, the signal in each subband can be characterized by the extracted features more effectively. The proposed method is applied to analyze the vibration signal measured from a faulty bearing. Testing results confirm that the proposed method is effective in extracting weak fault feature from a complex background.

scholarly journals Rolling-Element Bearing Fault Data Automatic Clustering Based on Wavelet and Deep Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yanli Yang ◽  
Peiying Fu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Wavelet Packet ◽  
Rolling Element Bearing ◽  
Classification Error ◽  
Bearing Fault ◽  
Automatic Clustering ◽  
Rolling Element ◽  
Element Bearing ◽  
The Time Domain

A method based on wavelet and deep neural network for rolling-element bearing fault data automatic clustering is proposed. The method can achieve intelligent signal classification without human knowledge. The time-domain vibration signals are decomposed by wavelet packet transform (WPT) to obtain eigenvectors that characterize fault types. By using the eigenvectors, a dataset in which samples are labeled randomly is configured. The dataset is roughly classified by the distance-based clustering method. A fine classification process based on deep neural network is followed to achieve accurate classification. The entire process is automatically completed, which can effectively overcome the shortcomings such as low work efficiency, high implementation cost, and large classification error caused by individual participation. The proposed method is tested with the bearing data provided by the Case Western Reserve University (CWRU) Bearing Data Center. The testing results show that the proposed method has good performance in automatic clustering of rolling-element bearings fault data.

A multichannel fusion approach based on coupled hidden Markov models for rolling element bearing fault diagnosis

2011 ◽  
Vol 226 (1) ◽  
pp. 202-216 ◽  
Author(s):  
W B Xiao ◽  
J Chen ◽  
G M Dong ◽  
Y Zhou ◽  
Z Y Wang
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Wavelet Packet ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Coupled Hidden Markov Models ◽  
Element Bearing ◽  
Fusion Approach

This paper presents a novel multichannel fusion approach based on coupled hidden Markov models (CHMMs) for rolling element bearing fault diagnosis. Different from a hidden Markov model (HMM), a CHMM contains multiple state sequences and observation sequences, and hence has powerful potential for multichannel fusion. In this study, a two-chain CHMM is employed to integrate the two-channel vibration signals collected from bearings, i.e. the horizontal and vertical vibration signals. Efficient probabilistic inference and parameter estimation algorithms are developed for the model. An experiment was carried out to validate the proposed approach. Normalized wavelet packet energy and wavelet packet energy entropy are extracted as features for classification respectively. Then, the results of the proposed approach are compared with those of the currently used approach based on HMMs and one-channel signals. The results show that the proposed approach is feasible and effective to improve the classification rate.

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