Abstract
As the central component of prognostic and health management (PHM) field, remaining useful life (RUL) estimation approaches based on degradation modeling have played an extremely significant role in recent years. For the newly developed systems working in complex environments, the associated degradation processes not only lack historical data and prior information but also have strong nonlinearity and three-source variability. Therefore, this paper proposes an adaptive RUL estimation approach for the newly developed system based on a nonlinear model. Specifically, a general nonlinear Wiener-process-based degradation model is established to simultaneously characterize three-source variability and nonlinearity, and the associated RUL distribution is derived with an explicit form. In order to utilize the condition monitoring (CM) data of the service system up to date, we present a parameter estimation method based on the expectation maximization algorithm to adaptively estimate and update the model parameters online. As such, the RUL distribution can be updated once the new CM data are available. Finally, the effectiveness and superiority of the proposed method are demonstrated by the numerical example an empirical study for battery data. The results show that the proposed method can provide accurate and robust RUL prediction for the newly developed system.
Highlights
With the rapid development of information technology, nonlinearity and complexity have already become two important trends contributing to the engineering equipment [1]
We develop an adaptive remaining useful life (RUL) estimation approach based on the nonlinear degradation model with three-source variability
The superiority and effectiveness of the proposed method are demonstrated by the numerical example and empirical study for battery data
Summary
With the rapid development of information technology, nonlinearity and complexity have already become two important trends contributing to the engineering equipment [1]. From the perspectives of safety, reliability and economy, the RUL estimation method based on performance degradation data for degradation modeling has been rapidly developed and widely applied due to its flexibility in application, and has occupied a dominant position in the PHM field, such as in the examples in [3]–[9], [11]–[13], and [20]–[23]. The above approaches are no longer applicable to the newly developed system with insufficient historical degradation data, and the related research is very limited up to now These investigations motivate the study of this paper, that is, developing an adaptive RUL estimation approach for newly developed system based on the nonlinear degradation model. We develop an adaptive RUL estimation approach based on the nonlinear degradation model with three-source variability. The primary task of estimating RUL is based on the CM dataset Y1:k to derive the conditional probability density function (PDF) fLk |Y1:k (lk |Y1:k )
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