Abstract
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.
Highlights
The condition-based maintenance (CBM) is a widely used maintenance policy which schedules the maintenance for components or systems according to the condition monitoring data [1]
Where p(zk | s1:k-1) is the prior probability density function (PDF) defined by the process model, p(sk | zk) is the likelihood determined by the observation model, and p(sk | s1:k-1) is the evidence, which can be described as follows: p = ∫ p p dzk. (5)
This paper presents a new remaining useful life (RUL) prediction method for bearings based on a Wiener process model
Summary
The condition-based maintenance (CBM) is a widely used maintenance policy which schedules the maintenance for components or systems according to the condition monitoring data [1]. Si [27] presented an adaptive prognostics method based on the nonlinear model and applied it to battery RUL prediction These models are usually not suitable for practical mechanical systems whose deterioration process is more complicated than the battery degradation. Based on the previous work, in this paper, we attempt to properly separate the bearings’ two working stages and model the degradation process to avoid unnecessary computational cost and obtain accurate RUL prediction results. Regarding the work of uncertainty management in the mechanical area, Zhao et al [6] used the Paris law to describe the development of the gear crack and updated the two correlated parameters in the model It was a simulation based framework and the uncertainties resulting from process noise and measurement error were neglected.
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