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. INDEX TERMS Remaining useful life, nonlinear, three-source variability, battery, prognostic and health management.

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Remaining useful life (RUL) estimation is the key of prognostics and health management (PHM) technology and is an effective way to ensure the safe and reliable operation of equipment. Aiming at the lack of historical data and prior information for the newly developed small-sample systems, an adaptive RUL estimation method based on the expectation maximization (EM) algorithm is proposed with threesource variability. First, a degradation model based on a Wiener process is established to incorporate threesource variability and dynamic sampling interval, and the analytical solution of RUL distribution is derived in the sense of the first hitting time. Second, an adaptive parameter estimation method based on the EM algorithm is proposed to update the model parameters by using the condition monitoring (CM) data from one working system running up to the current moment. Finally, a practical example of a gyroscope in an inertial navigation system is provided to substantiate the effectiveness and superiority of the proposed method. The results indicate that the proposed method can efficiently improve the accuracy of the RUL estimation. INDEX TERMS Remaining useful life, three-source variability, expectation maximization, dynamic sampling interval.

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