Hidden Markov Models and their Application for Predicting Failure Events

TL;DR

This paper introduces a hierarchical mixture model approach using Markov models to predict asset failure, improving regularization, computational efficiency, and transfer learning capabilities for degradation prediction.

Contribution

It proposes a novel use of hierarchical mixture distributions within Markov models for asset degradation prediction, enabling better regularization and transfer learning.

Findings

Hierarchical mixtures improve prediction accuracy.

Shared mixtures reduce computational complexity.

Combining MMMM with POMDP optimizes maintenance policies.

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 a separate distribution for each hidden state, we use hierarchical mixtures of distributions in 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 the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.