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A Profust Reliability Based Approach to Prognostics and Health Management

机译：基于专家可靠度的预测和健康管理方法

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摘要

Prognostics and health management (PHM) technology has been widely accepted, and employed to evaluate system performance. In practice, system performance often varies continually rather than just being functional or failed, especially for a complex system. Profust reliability theory extends the traditional binary state space ${0, 1}$ into a fuzzy state space $[0, 1]$, which is therefore suitable to characterize a gradual physical degradation. Moreover, in profust reliability theory, fuzzy state transitions can also help to describe the health evolution of a component or a system. Accordingly, this paper proposes a profust reliability based PHM approach, where the profust reliability is employed as a health indicator to evaluate the real-time system performance. On the basis of the health estimation, the system remaining useful life (RUL) is further defined, and the mean RUL estimate is predicted by using a degraded Markov model. Finally, an experimental case study of Li-ion batteries is presented to demonstrate the effectiveness of the proposed approach.
机译：预后和健康管理（PHM）技术已被广泛接受，并用于评估系统性能。实际上，系统性能通常会不断变化，而不仅仅是功能或故障，特别是对于复杂的系统。可靠的可靠性理论将传统的二进制状态空间$ {0，1} $扩展为模糊状态空间$ [0，1] $，因此适合表征逐渐的物理退化。此外，在可靠的可靠性理论中，模糊状态转换还可以帮助描述组件或系统的运行状况。因此，本文提出了一种基于可靠度的PHM方法，该可靠度被用作健康指标来评估实时系统性能。在健康评估的基础上，进一步定义了系统剩余使用寿命（RUL），并使用降级马尔可夫模型预测了平均RUL评估。最后，提出了锂离子电池的实验案例研究，以证明所提出方法的有效性。

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