Enhancement of bearing fault detection using an alternative analytic energy operator and sparse Bayesian step-filtering

2021 ◽  
Author(s):  
Yan Wang ◽  
Lichen Gu ◽  
Yuanbo Xu
Keyword(s):  
Fault Detection ◽  
Energy Operator ◽  
Bearing Fault ◽  
Bearing Fault Detection

A Joint Kurtosis-Based Adaptive Bandstop Filtering and Iterative Autocorrelation Approach to Bearing Fault Detection

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Yi Zhang ◽  
Ming Liang ◽  
Chuan Li ◽  
Shumin Hou
Keyword(s):  
Experimental Data ◽  
Fault Detection ◽  
Detection Method ◽  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Energy Operator ◽  
Signal To Noise ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Selection Of

This paper reports a bearing fault detection method based on kurtosis-based adaptive bandstop filtering (KABS) and iterative autocorrelation (IAC). The interferences in the bearing signal can be removed by KABS filtering, whereas IAC is employed for noise reduction and signal enhancement. In the KABS method, two window-merging schemes are proposed to identify the frequency bands potentially containing interferences and to preserve those covering fault frequencies. Issues related to the selection of the number of autocorrection iterations are also discussed. The proposed method can be used for bearing fault detection in a low signal-to-noise ratio (SNR) and low signal-to-interference ratio (SIR) environment. The implementation of the proposed method does not require prior knowledge of the fault-excited resonant frequency. The performance of the proposed method has been examined by simulation analysis, with favorable comparisons to the Hilbert enveloping, energy operator, and spectrum kurtosis methods. Its effectiveness in bearing fault detection has also been demonstrated using experimental data.

BEARING FAULT DETECTION AND DIAGNOSIS BASED ON TEAGER–HUANG TRANSFORM

2009 ◽  
Vol 07 (05) ◽  
pp. 643-663 ◽  
Author(s):  
HUI LI ◽  
HAIQI ZHENG ◽  
LIWEI TANG
Keyword(s):  
Fault Detection ◽  
Modulation Frequency ◽  
Energy Operator ◽  
Vibration Signal ◽  
Mean Amplitude ◽  
Fault Detection And Diagnosis ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Detection

A new approach to fault diagnosis of bearings based on the Teager–Huang Transform (THT) is presented. This method is based on the Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) techniques. EMD can adaptively decompose the vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM) Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager–Huang Transform approach is better than that of the Hilbert–Huang Transform approach for bearing fault detection. The Teager–Huang Transform has better resolution than the Hilbert–Huang Transform. The Teager–Huang Transform can effectively diagnose the faults of the bearing, thus providing a viable processing tool for gearbox defect monitoring.

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