A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine

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.

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A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406215593568 ◽

2015 ◽

Vol 230 (13) ◽

pp. 2314-2322 ◽

Cited By ~ 15

Author(s):

Keheng Zhu ◽

Haolin Li

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Fuzzy Entropy ◽

Rolling Element Bearing ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Rolling Element ◽

Element Bearing ◽

Bearing Vibration

Aiming at the non-linear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a new rolling element bearing fault diagnosis method based on hierarchical fuzzy entropy and support vector machine is proposed in this paper. By incorporating the advantages of both the concept of fuzzy sets and the hierarchical decomposition of hierarchical entropy, hierarchical fuzzy entropy is developed to extract the fault features from the bearing vibration signals, which can provide more useful information reflecting bearing working conditions than hierarchical entropy. After feature extraction with hierarchical fuzzy entropy, a multi-class support vector machine is trained and then employed to fulfill an automated bearing fault diagnosis. The experimental results demonstrate that the proposed approach can identify different bearing fault types as well as severities precisely.

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Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2005.05.002 ◽

2006 ◽

Vol 20 (7) ◽

pp. 1523-1536 ◽

Cited By ~ 65

Author(s):

Alfonso Rojas ◽

Asoke K. Nandi

Keyword(s):

Support Vector Machines ◽

Rolling Element Bearing ◽

Support Vector ◽

Bearing Faults ◽

Fast Detection ◽

Vector Machines ◽

Rolling Element ◽

Element Bearing ◽

Practical Scheme

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Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine

Noise & Vibration Worldwide ◽

10.1177/0957456519883280 ◽

2019 ◽

Vol 50 (9-11) ◽

pp. 313-327 ◽

Cited By ~ 1

Author(s):

Chandrabhanu Malla ◽

Ankur Rai ◽

Vaishali Kaul ◽

Isham Panigrahi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Morlet Wavelet ◽

Rolling Element Bearing ◽

Support Vector ◽

Bearing Fault ◽

Rolling Element ◽

Element Bearing ◽

Complex Morlet Wavelet

Condition monitoring and fault diagnosis of rolling element bearings are very important to ensure proper working of different types of machinery. Condition monitoring of rotating machines is mainly based on the analysis of machine vibration. The vibration signals from the mechanical fault generally comprise periodic impulses with specified characteristic frequency corresponds to a particular defect. But due to heavy noise in the industry, the vibration signals have a very low signal-to-noise ratio. Hence, it requires an appropriate technique to extract the impulses from the noisy signal. This article emphasized on the fault diagnosis of rolling element bearings having some specific size of defects on various bearing elements using the complex Morlet wavelet analysis. The phase and amplitude map of the complex Morlet wavelet are utilized for identification and diagnosis of the fault in the rolling element bearing. The amplitude and phase map corresponding to the complex Morlet wavelet are found to show unique informative signatures in the presence of bearing faults. The classification technique based on artificial neural network and support vector machine for rolling element bearing fault detection is presented in this article. The classification results of bearing faults clearly indicate that support vector machine has a more precise bearing fault classification ability than artificial neural network.

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