Bearing Remaining Useful Life Prediction Based on a Nonlinear Wiener Process Model

Autor: Juan Wen, Hongli Gao, Jiangquan Zhang
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Shock and Vibration, Vol 2018 (2018)
Druh dokumentu: article
ISSN: 1070-9622
1875-9203
DOI: 10.1155/2018/4068431
Popis: Prognostic is an essential part of condition-based maintenance, which can be employed to enhance the reliability and availability and reduce the maintenance cost of mechanical systems. This paper develops an improved remaining useful life (RUL) prediction method for bearings based on a nonlinear Wiener process model. First, the service life of bearings is divided into two stages in terms of the working condition. Then a new prognostic model is constructed to reflect the relationship between time and bearing health status. Besides, a variety of factors that cause uncertainties toward the degradation path are considered and appropriately managed to obtain reliable RUL prediction results. The particle filtering is utilized to estimate the degradation state, qualify the uncertainties, and predict the RUL. The experimental studies show that the proposed method has a better performance in RUL prediction and uncertainty management than the exponential model and the linear model.
Databáze: Directory of Open Access Journals
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