An estimating method for initial faulty time of rolling element bearing based on EEMD and spectral kurtosis

2013 ◽  
Author(s):  
Yuanyuan Kong ◽  
Yaobing Wei ◽  
Changfeng Yan ◽  
Li You
Keyword(s):  
Rolling Element Bearing ◽  
Spectral Kurtosis ◽  
Rolling Element ◽  
Element Bearing

scholarly journals Diagnostics 101: A Tutorial for Fault Diagnostics of Rolling Element Bearing Using Envelope Analysis in MATLAB

2020 ◽  
Vol 10 (20) ◽  
pp. 7302
Author(s):  
Seokgoo Kim ◽  
Dawn An ◽  
Joo-Ho Choi
Keyword(s):  
Process Simulation ◽  
Rolling Element Bearing ◽  
Fault Identification ◽  
New Developments ◽  
Envelope Analysis ◽  
Practical Applications ◽  
Spectral Kurtosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Simulation Signal

This paper presents a MATLAB-based tutorial to conduct fault diagnosis of a rolling element bearing. While there have been so many new developments in this field, no studies have addressed the tutorial aspects in this field to help the engineers learn the concept and implement by their own effort. The three most common techniques—the autoregressive model, spectral kurtosis, and envelope analysis—are selected to demonstrate the bearing diagnosis process. Simulation signal is introduced to help understand the characteristics of fault signal and carry out the process toward the fault identification. The techniques are then applied to the two real datasets to demonstrate the practical applications, one made by the authors and the other by the Case Western Reserve University, which is known as a standard reference in testing the diagnostic algorithms.

Fault Diagnosis of Metallurgical Machinery Based on Spectral Kurtosis and GA-SVM

2013 ◽  
Vol 634-638 ◽  
pp. 3958-3961 ◽  
Author(s):  
Yun Li ◽  
Yan Gao ◽  
Jun Guo ◽  
Xian Jun Yu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Simulation Study ◽  
High Accuracy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Spectral Kurtosis ◽  
Rolling Element ◽  
Element Bearing

This paper proposed a new method of rolling element bearing (REB) fault diagnosis for metallurgical machinery. Mainly it stresses on the combination of spectral kurtosis (SK) and supports vector machine (SVM), using genetic algorithm (GA) to optimize the parameters of support vector machine at the same time. Thus, this study aims to integrate SK, GA and SVM in order to develop an intelligent REB fault detector for metallurgical machineries. Simulation study indicates that this method can effectively detect the REB faults with a high accuracy.

scholarly journals Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 50 ◽  
Author(s):  
Xuejun Zhao ◽  
Yong Qin ◽  
Changbo He ◽  
Limin Jia ◽  
Linlin Kou
Keyword(s):  
Fault Diagnosis ◽  
Impulsive Noise ◽  
Real Data ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Resonance Band ◽  
Rolling Element ◽  
Element Bearing

Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.

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