论文标题
基于高阶隐藏半马尔科夫模型的健康评估和预后
Health Assessment and Prognostics Based on Higher Order Hidden Semi-Markov Models
论文作者
论文摘要
本文提出了一个新的灵活的预后框架，该框架基于具有不可观察到的健康状态和复杂过渡动态的系统或组件的高阶隐藏半马尔科夫模型（HOHSMM）。 HOHSMM通过允许隐藏状态取决于其更遥远的历史记录并假设通常分布的状态持续时间来扩展基本的隐藏马尔可夫模型（HMM）。有效的Gibbs采样算法设计用于HOHSMM的统计推断。通过进行仿真实验来评估所提出的HOHSMM采样器的性能。我们进一步设计了一种解码算法，以使用学习的模型来估计隐藏的健康状况。鉴于解码的隐藏状态，使用模拟方法预测剩余的使用寿命（RUR）。关于NASA Turbofan发动机的案例研究证明了拟议的预后框架的实际实用性。结果表明，基于HOHSMM的预后框架为复杂系统提供了良好的隐藏状态评估和规则估计。
This paper presents a new and flexible prognostics framework based on a higher order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective Gibbs sampling algorithm is designed for statistical inference of an HOHSMM. The performance of the proposed HOHSMM sampler is evaluated by conducting a simulation experiment. We further design a decoding algorithm to estimate the hidden health states using the learned model. Remaining useful life (RUL) is predicted using a simulation approach given the decoded hidden states. The practical utility of the proposed prognostics framework is demonstrated by a case study on NASA turbofan engines. The results show that the HOHSMM-based prognostics framework provides good hidden health state assessment and RUL estimation for complex systems.
