scholarly journals A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hu Aijun ◽  
Lin Jianfeng ◽  
Sun Shangfei ◽  
Xiang Ling
Keyword(s):  
Fault Detection ◽  
Finite Impulse Response ◽  
Rolling Element Bearing ◽  
Signal Extraction ◽  
Outer Race ◽  
Bearing Fault ◽  
Novel Approach ◽  
Rolling Element ◽  
Element Bearing ◽  
Early Fault Detection

The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH) is proposed to extract the impulsive signal and enhance the impulsiveness of the bearing fault signal, and the envelope analysis is applied to reveal the fault-related signatures. The impulsive extraction performance of the proposed CTH is compared with that of finite impulse response filter (FIR) by analyzing the simulated bearing fault signals, and the result indicates that the CTH is more effective in extracting impulsive signals. The method is evaluated using real fault signals from defective bearings with early rolling element fault and early fault located on the outer race. The results show that the proposed method is able to enhance the impulsiveness of early bearing fault signals.

Simulation Research on Rolling Element Bearing Fault Signal Extraction Based on Blind Source Separation

2014 ◽  
Vol 989-994 ◽  
pp. 3738-3742
Author(s):  
Yong Xiang Zhang ◽  
Jie Ping Zhu ◽  
Shuai Zhang
Keyword(s):  
Cost Function ◽  
Blind Source Separation ◽  
Source Separation ◽  
Hessian Matrix ◽  
Rolling Element Bearing ◽  
Signal Extraction ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
The Cost

In order to extract the fault information from rolling element bearing, combined with Kurtosis criteria and Hessian matrix. An improved rolling element bearing fault signal extraction method is proposed. Kurtosis is the cost function. The method is according to the construction principles of blind source separation (BSS), and it uses an analytically derived Hessian matrix in the maximization process of the cost function used. Then the impact signal is extracted successfully. The effectiveness of the method is demonstrated on simulated signal.

A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis

2012 ◽  
Vol 227 (6) ◽  
pp. 1362-1370 ◽  
Author(s):  
Changqing Shen ◽  
Qingbo He ◽  
Fanrang Kong ◽  
Peter W Tse
Keyword(s):  
Fault Diagnosis ◽  
Morphological Analysis ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Characteristic

The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.

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.

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