Hidden Markov Models and their Application for Predicting Failure Events

EasyChair Preprint 3183

Abstract

We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of using a separate distribution for each hidden state we are using hierarchical mixtures of distributions of the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between between the risk of failure and extended operating hours by combining a hidden Markov Model (HMM) with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.

Keyphrases: Hidden Markov Model, Hierarchical Mixture Models, POMDP, Predictive Maintenance, Reinforcement Learning, asset degradation., failure prediction, optimal decision making under uncertainty, predicting failure event, time series prediction

@booklet{EasyChair:3183,
  author    = {Paul Hofmann and Zaid Tashman},
  title     = {Hidden Markov Models and their Application for Predicting Failure Events},
  howpublished = {EasyChair Preprint 3183},
  year      = {EasyChair, 2020}}