The fault detection and diagnosis in rolling element bearings using frequency band entropy

2012 ◽  
Vol 227 (1) ◽  
pp. 87-99 ◽  
Author(s):  
Tao Liu ◽  
Jin Chen ◽  
Guangming Dong ◽  
Wenbing Xiao ◽  
Xuning Zhou
Keyword(s):  
Frequency Band ◽  
Frequency Component ◽  
Fault Detection And Diagnosis ◽  
Rolling Element Bearings ◽  
Rolling Bearings ◽  
Rolling Element ◽  
Band Pass ◽  
Detection And Diagnosis ◽  
Short Time ◽  
Fault Feature Extraction

In vibration analysis, fault feature extraction from strong background noises is of great importance. Frequency band entropy based on short-time Fourier transform illustrates the complexity of every frequency component in the frequency domain, and it can be used to detect the periodical components hidden in the signal. This article shows how the frequency band entropy offers a robust way in detecting faults even when the signal is under strong masking noises. Furthermore, frequency band entropy provides a way of blindly designing optimal band-pass filters. The filtering signal combined with envelope analysis is helpful in fault diagnosis. The effectiveness of the proposed method is demonstrated on both simulated and actual data from rolling bearings.

The Effect of Wear Evolution on Vibration-based Fault Detection in Tapered Roller Bearings

2019 ◽  
Vol 9 (1) ◽  
pp. 18-23
Author(s):  
K Rabeyee ◽  
X Tang ◽  
F Gu ◽  
A D Ball
Keyword(s):  
Condition Monitoring ◽  
Rolling Element Bearings ◽  
Rolling Bearings ◽  
Roller Bearings ◽  
Fault Type ◽  
Rolling Element ◽  
Tapered Roller ◽  
Magnitude Variation ◽  
Tapered Roller Bearings ◽  
The Impact

Rolling element bearings (REBs) are typical tribological components used widely in rotating machines. Their failure could cause catastrophic damage. Therefore, condition monitoring of bearings has always had great appeal for researchers. Usually, the detection and diagnostics of incipient bearing faults are achieved by characterising the weak periodic impacts induced by the collision of defective bearing components. However, race wear evolution, which is inevitable in bearing applications, can affect the contact between bearing elements and races, thereby decreasing the impact magnitudes and impeding detection performance. In this paper, the effect of wear evolution on the condition monitoring of rolling bearings is firstly analysed based on internal clearance changes resulting from the wear effect. Then, an experimental study is ingeniously designed to simulate wear evolution and evaluate its influence on wellknown envelope signatures according to measured vibrations from widely used tapered roller bearings. The fault type is diagnosed in terms of two indices: the magnitude variation of characteristic frequencies and the deviation of such frequencies. The experimental results indicate a signature decrease with regard to wear evolution, suggesting that accurate severity diagnosis needs to take into account both the wear conditions of the bearing and the signature magnitudes.

scholarly journals Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1845 ◽  
Author(s):  
Xiaohui Gu ◽  
Shaopu Yang ◽  
Yongqiang Liu ◽  
Rujiang Hao ◽  
Zechao Liu
Keyword(s):  
Frequency Band ◽  
Background Noise ◽  
Grey Wolf Optimizer ◽  
Data Sets ◽  
Rolling Element Bearings ◽  
Grey Wolf ◽  
Objective Functions ◽  
Multi Objective ◽  
Rolling Element ◽  
Envelope Spectrum

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

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.

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