[This paper was initially published in PHME conference in 2016, selected for further publication in International Journal of Prognostics and Health Management.] This paper describes an Autoregressive Partially-hidden Markov model (ARPHMM) for fault detection and prognostics of equipments based on sensors' data. It is a particular dynamic Bayesian network that allows to represent the dynamics of a system by means of a Hidden Markov Model (HMM) and an autoregressive (AR) process. The Markov chain assumes that the system is switching back and forth between internal states while the AR process ensures a temporal coherence on sensor measurements. A sound learning procedure of standard ARHMM based on maximum likelihood allows to iteratively estimate all parameters simultaneously. This paper suggests a modification of the learning procedure considering that one may have prior knowledge about the structure which becomes partially hidden. The integration of the prior is based on the Theory of Weighted Distributions which is compatible with the Expectation-Maximization algorithm in the sense that the convergence properties are still satisfied. We show how to apply this model to estimate the remaining useful life based on health indicators. The autoregressive parameters can indeed be used for prediction while the latent structure can be used to get information about the degradation level. The interest of the proposed method for prognostics and health assessment is demonstrated on CMAPSS datasets.
翻译:[本文件最初在2016年PHME会议上发表,并被选中在《预测和健康管理国际期刊》上进一步出版。 ]本文描述了基于传感器数据的设备故障检测和预测的自动递减部分隐藏的Markov模型(ARPHMMM),这是一个特殊的动态巴伊西亚网络,能够通过隐藏的Markov模型(HMM)和自动递增(AR)进程来代表系统的动态。Markov链条假定该系统在内部各州之间发生回转,而AR进程则确保传感器测量的时间一致性。基于最大可能性的ARHMM标准的健全学习程序可以同时迭接地估计所有参数。本文建议修改学习程序,因为一个人可能事先了解部分隐藏的结构。 先前的整合基于与预期-最大递增(AR)进程兼容的Weightd 分布原理。我们展示了该模型如何在估算其余有用生命时,同时根据健康状况指标进行估算。