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Entropy 2016, 18(11), 418; doi:10.3390/e18110418

Prediction of Bearing Fault Using Fractional Brownian Motion and Minimum Entropy Deconvolution

1
College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2
School of Information Science and Technology, East China Normal University, Shanghai 200241, China
3
Ocean College, Zhejiang University, Hangzhou 316021, China
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 24 October 2016 / Revised: 12 November 2016 / Accepted: 18 November 2016 / Published: 23 November 2016
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory II)
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Abstract

In this paper, we propose a novel framework for the diagnosis of incipient bearing faults and trend prediction of weak faults which result in gradual aggravation with the bearing vibration intensity as the characteristic parameter. For the weak fault diagnosis, the proposed framework adopts the improved minimum entropy deconvolution (MED) theory to identify the weak fault characteristics of mechanical equipment. From a large number of actual data analysis, once a bearing shows a weak fault, the bearing vibration intensity not only has random non-stationary, but also long-range dependent (LRD) characteristics. Therefore, the stochastic model with LRD−fractional Brown motion (FBM) is proposed to evaluate and predict the condition of slowly varying bearing faults which is a gradual process from weak fault occurrence to severity. For the FBM stochastic model, we mainly implement the derivation and the parameter identification of the FBM model. This is the first study to slowly fault prediction with stochastic model FBM. Experimental results show that the proposed methods can obtain the best performance in incipient fault diagnosis and bearing condition trend prediction. View Full-Text
Keywords: long-range dependence (LRD); minimum entropy deconvolution (MED); vibration intensity; fractional Brownian motion (FBM); prediction of bearing fault long-range dependence (LRD); minimum entropy deconvolution (MED); vibration intensity; fractional Brownian motion (FBM); prediction of bearing fault
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Song, W.; Li, M.; Liang, J.-K. Prediction of Bearing Fault Using Fractional Brownian Motion and Minimum Entropy Deconvolution. Entropy 2016, 18, 418.

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