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Algorithms 2018, 11(7), 89; https://doi.org/10.3390/a11070089

Degradation Trend Prediction for Rotating Machinery Using Long-Range Dependence and Particle Filter Approach

1
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
2
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA
*
Author to whom correspondence should be addressed.
Received: 29 April 2018 / Revised: 6 June 2018 / Accepted: 14 June 2018 / Published: 26 June 2018
(This article belongs to the Special Issue Stochastic Optimization: Algorithms and Applications)
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Abstract

Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating machinery, taking the rolling bearing as an example. In this work, the degradation prediction is evaluated based on two health indicators time series; i.e., equivalent vibration severity (EVI) time series and kurtosis time series. Specifically, the degradation trend prediction issues here addressed have the following two distinctive features: (i) EVI time series with weak LRD property and (ii) kurtosis time series with sharp transition points (STPs) in the forecasted region. The core idea is that the parameters distribution of the LRD model can be updated recursively by the particle filter algorithm; i.e., the parameters degradation of the LRD model are restrained, and thus the prognostic results could be generated real-time, wherein the initial LRD model is designed randomly. The prediction results demonstrate that the significant improvements in prediction accuracy are obtained with the proposed method compared to some state-of-the-art approaches such as the autoregressive–moving-average (ARMA) model and the fractional order characteristic (FOC) model, etc. View Full-Text
Keywords: long-range dependence (LRD) model; particle filter (PF); equivalent vibration severity (EVI); kurtosis; Hurst exponent; rotating machinery long-range dependence (LRD) model; particle filter (PF); equivalent vibration severity (EVI); kurtosis; Hurst exponent; rotating machinery
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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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Li, Q.; Liang, S.Y. Degradation Trend Prediction for Rotating Machinery Using Long-Range Dependence and Particle Filter Approach. Algorithms 2018, 11, 89.

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