Fault feature extraction based on combination of envelope order tracking and cICA for rolling element bearings

2018 ◽  
Vol 113 ◽  
pp. 131-144 ◽  
Author(s):  
Tangfeng Yang ◽  
Yu Guo ◽  
Xing Wu ◽  
Jing Na ◽  
Rong-Fong Fung
Keyword(s):  
Feature Extraction ◽  
Rolling Element Bearings ◽  
Order Tracking ◽  
Rolling Element ◽  
Fault Feature Extraction

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.

Fault Feature Extraction Method of the Rolling Element Bearing Based on Reverse Order Tracking

2016 ◽  
Vol 693 ◽  
pp. 1361-1370
Author(s):  
De Zun Zhao ◽  
Wei Dong Cheng ◽  
Wei Gang Wen ◽  
Yang Liu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Rolling Element Bearing ◽  
Reverse Order ◽  
Time Signal ◽  
Feature Extraction Method ◽  
Order Tracking ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Feature Extraction

When dealing with the vibration analysis of the rolling element bearing under gear noise and time-varying speed condition, order tracking is always utilized to convert the time signal to angular domain. In this way, the smearing effect in the spectrum is avoided and the noise cancellation methods based on the periodicity of the gear signal can be reapplied. In this paper, the resonance frequency variation of the resampled signal is analyzed and its influence on the kurtogram algorithm based bandpass filtering procedure is studied through a simulation experiment and a fault feature extraction method of the rolling bearing based on reverse order tracking is proposed. Effectiveness of the proposed method is verified through the analysis of the signal measured from the test-rig.

scholarly journals Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method

Measurement ◽  
2019 ◽  
Vol 139 ◽  
pp. 226-235 ◽  
Author(s):  
Junchao Guo ◽  
Dong Zhen ◽  
Haiyang Li ◽  
Zhanqun Shi ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Noise Reduction ◽  
Reduction Method ◽  
Rolling Element Bearing ◽  
Multi Stage ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Feature Extraction

Envelope order tracking for fault detection in rolling element bearings

2012 ◽  
Vol 331 (25) ◽  
pp. 5644-5654 ◽  
Author(s):  
Yu Guo ◽  
Ting-Wei Liu ◽  
Jing Na ◽  
Rong-Fong Fung
Keyword(s):  
Fault Detection ◽  
Rolling Element Bearings ◽  
Order Tracking ◽  
Rolling Element

Vibration Analysis Based Feature Extraction for Bearing Fault Detection

2012 ◽  
Vol 197 ◽  
pp. 124-128
Author(s):  
Jie Liu ◽  
Chun Sheng Yang ◽  
Qing Feng Lou
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Rolling Element Bearings ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Rotary Machine ◽  
Catastrophic Failures ◽  
Rolling Element ◽  
Significant Research ◽  
Recent Developments

Rolling element bearings are widely used in various rotary machines. Most rotary machine failures are attributed to unexpected bearing faults. Accordingly, reliable bearing fault detection is critically needed in industries to prevent these machines’ performance degradation, malfunction, or even catastrophic failures. Feature extraction plays an important role in bearing fault detection and significant research efforts have thus far been devoted to this subject from both academia and industry. This paper intends to provide a brief review of the recent developments in feature extraction for bearing fault detection, and the focus will be placed on the advances in methods for dealing with the nonstationary characteristics of bearing fault signatures.

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