A Survey of HMM-based Algorithms in Machinery Fault Prediction
Somayeh Bakhtiari Ramezani, Brad Killen, Logan Cummins, Shahram Rahimi, Amin Amirlatifi, Maria Seale
Abstract
Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase the system's lifespan, reliability, and availability. Different techniques are used in the literature to determine the health state of the system, one of which is the Hidden Markov Models (HMMs). This class of algorithms is very well suited for modeling the health condition dictated by the latent states of the system. HMMs can reveal transitions from one state to another, thus highlighting degradation in a system's health and the right time for maintenance. While many extensions and variations of the HMM are studied for a variety of applications, the present study aims to evaluate and compare the state-of-the-art HMM-based research in predictive maintenance only. This study also aims to discuss the capabilities and limitations of such algorithms and future directions to tackle the current limitations.