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- W2888891666 abstract "The health stage division problem has been attracting interest for its potential role in Prognostics and Health Management (PHM). In traditional health stage division, multiple health indicators (HIs) are extracted from original data, and then one suitable HI is constructed. According to the trend of the one HI, the whole degradation process is divided into different health stages based on the change points. The change points are the transition points between different health stages. But the one HI is difficult to construct and just suitable for special applications. Therefore how to consider multiple HIs simultaneously for health stage division is a big problem. In this paper, a reinforcement learning-based method by using change points (RLCP) is proposed to divide health state. The health stage division problem based on change points is modelled as a Markov Decision Process (MDP). Then reinforcement learning is introduced to solve the MDP effectively. The performance of the proposed RLCP is evaluated with the PRONOSTIA dataset. Experiment results show that the result of health stage division is optimal when compared with existing methods." @default.
- W2888891666 created "2018-09-07" @default.
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- W2888891666 date "2018-06-01" @default.
- W2888891666 modified "2023-09-24" @default.
- W2888891666 title "RLCP: A Reinforcement Learning Method for Health Stage Division Using Change Points" @default.
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- W2888891666 doi "https://doi.org/10.1109/icphm.2018.8448499" @default.
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