1551. Remaining useful life prediction of rolling bearings by the particle filter method based on degradation rate tracking
Bin Fan1, Lei Hu2, Niaoqing Hu3
Science and Technology on Integrated Logistics
E-mail: email@example.com, firstname.lastname@example.org, email@example.com
(Received 9 December 2014; received in revised form 7 February 2015; accepted 10 March 2015)
Abstract. There is no doubt that remaining useful life prediction is important to the health management of modern mechanical equipment. But in most cases, the useful operational information of equipment we can get are limited, one of them is vibration signal. Particle filter is a hybrid prediction method combined with data-driven and model-based two kinds of methods. It can solve prognosis problem with the fitted prediction model only by historical data, and allow the uncertainty management. However, the prediction performance of the method is largely dependent on the prediction model and very sensitive to the initial distribution of the model parameters. These flaws limit the further development of particle filter methods in the prediction. Aiming at the shortcomings of the basic particle filter prediction method, a general prediction framework of particle filter based on degradation rate tracking is proposed in this paper. It turned away from the fitted model, and utilized the statistical rule of degradation rate of historical data to estimate and predict the degradation process of system. The effectiveness of the method proposed is validated with useful life prediction case of rolling bearings.
Keywords: particle filter, vibration signal, degradation rate tracking, rolling bearing, remaining useful life prediction.
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Cite this article
Fan Bin, Hu Lei, Hu Niaoqing Remaining useful life prediction of rolling bearings by the particle filter method based on degradation rate tracking. Journal of Vibroengineering, Vol. 17, Issue 2, 2015, p. 743‑756.
© JVE International Ltd. Journal of Vibroengineering. Mar 2015, Volume 17, Issue 2. ISSN 1392-8716