scholarly journals Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

2019 ◽  
Vol 9 (9) ◽  
pp. 1876 ◽  
Author(s):  
Zheng Li ◽  
Anbo Ming ◽  
Wei Zhang ◽  
Tao Liu ◽  
Fulei Chu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Energy Operator ◽  
Segmentation Result ◽  
Rolling Element Bearings ◽  
Empirical Wavelet Transform ◽  
Rolling Element ◽  
Fault Feature Extraction

In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.

Hybrid fault-feature extraction of rolling element bearing via customized-lifting multi-wavelet packet transform

2013 ◽  
Vol 228 (12) ◽  
pp. 2204-2216 ◽  
Author(s):  
Qiang Liao ◽  
Xunbo Li ◽  
Bo Huang
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Rolling Element Bearing ◽  
Feature Extraction Method ◽  
Rolling Element ◽  
Localized Defects ◽  
Element Bearing ◽  
Diagnostic Framework ◽  
Fault Feature Extraction

The rolling element bearing is one of the most extensively used components in various rotating machinery, and it is therefore critical to develop a suitable online rolling element bearing fault-diagnostic framework to improve a rolling element bearing system’s failure protection during conditional operations. In this paper, a hybrid fault-feature extraction method by detecting localized defects and analyzing vibration signals of rolling element bearings via customized multi-wavelet packet transform is proposed, in which the swarm fish algorithm has been utilized for the minimization of signal residual to determine the adaptive prediction operator. With good properties of concurrent symmetry, orthogonality, short support and high-order vanishing moment, the multiple wavelet functions and scaling functions are defined for the hybrid fault-feature extraction, which match the diverse characteristics of hybrid fault and extract coupling features, and the proposed lifting scheme-based multi-wavelet packet transform is highly effective. Then, the proposed method is validated by rolling element bearing experimental results, which show that this approach can effectively extract the hybrid fault features of inner race and rolling element.

scholarly journals The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jiyong Li ◽  
Shunming Li ◽  
Xiaohong Chen ◽  
Lili Wang
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Least Square ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Kernel Independent Component Analysis ◽  
Rolling Element ◽  
Independent Features

Rolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and superfluous features may degrade the classification performance, it is needed to extract independent features, so LSSVM (least square support vector machine) based on hybrid KICA-GDA (kernel independent component analysis-generalized discriminate analysis) is presented in this study. A new method named sensitive subband feature set design (SSFD) based on wavelet packet is also presented; using proposed variance differential spectrum method, the sensitive subbands are selected. Firstly, independent features are obtained by KICA; the feature redundancy is reduced. Secondly, feature dimension is reduced by GDA. Finally, the projected feature is classified by LSSVM. The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. Experimental results from two different fault-seeded bearing tests show good performance of the proposed method.

A Combination of WKNN to Fault Diagnosis of Rolling Element Bearings

2009 ◽  
Vol 131 (6) ◽  
Author(s):  
Yaguo Lei ◽  
Zhengjia He ◽  
Yanyang Zi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Wavelet Packet ◽  
Rolling Element Bearings ◽  
K Nearest Neighbor ◽  
Vibration Signals ◽  
Multiple Classifiers ◽  
Operation Conditions ◽  
Rolling Element

This paper presents a new method for fault diagnosis of rolling element bearings, which is developed based on a combination of weighted K nearest neighbor (WKNN) classifiers. This method uses wavelet packet transform based on the lifting scheme to preprocess the vibration signals before feature extraction. Time- and frequency-domain features are all extracted to represent the operation conditions of the bearings totally. Sensitive features are selected after feature extraction. And then, multiple classifiers based on WKNN are combined to overcome the two disadvantages of KNN and therefore it may enhance the classification accuracy. The experimental results of the proposed method to fault diagnosis of the rolling element bearings show that this method enables the detection of abnormalities in bearings and at the same time identification of fault categories and levels.

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