Prognostic activity deals with prediction of the remaining useful life (RUL) of physical systems, based on their actual health state and their usage conditions. RUL prediction gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. In addition, it can be used to improve the characterization of the material properties, that govern damage propagation for the monitored structure. RUL can be predicted by using three main approaches, namely model-based, datadriven and hybrid approaches. Prognostic methods used later in this paper are hybrid and data-driven approaches, which employ the Particle Filter in the first one and the autoregressive integrated moving average in the second. The performance of the suggested approaches is evaluated in a comparative study on data collected from lithium-ion battery of hybrid electric vehicle.

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Abstract. All industrial systems and machines are subjected to degradation processes, which can be related to the operating conditions. This degradation can cause unwanted stops at any time and major maintenance work sometimes. The accurate prediction of the remaining useful life (RUL) is an important challenge in condition-based maintenance. Prognostic activity allows estimating the RUL before failure occurs and triggering actions to mitigate faults in time when needed. In this study, a new smart prognostic method for photovoltaic module health degradation was developed based on two approaches to achieve more accurate predictions: online diagnosis and data-driven prognosis. This framework of forecasting integrates the strengths of real-time monitoring in the first approach and relevant vector machine in the second. The results show that the proposed method is plausible due to its good prediction of RUL and can be effectively applied to many systems for monitoring and prognostics.

Purpose The purpose of this paper is to create a new method of prognosis based on remaining useful life (RUL) prediction for degradation assessment. Design/methodology/approach In the present paper the authors describe a new method of prognosis to improve the accuracy of forecasting the system state. This framework of forecasting integrates the model-based information and the hybrid approach, which employs the structured residuals in the first part and the particle filter in the second part. Findings The performance of the suggested fusion framework is employed to predict the RUL of battery pack in hybrid electric vehicle. The results show that the proposed method is plausible due to the good prediction of RUL, and can be effectively applied to many systems for prognosis. Originality/value In this study the authors illustrate how the suggested method can provide an accurate prediction of the RUL over conventional data-driven methods without physical model and classical particle filter with a single damage model.