Impact of Maintenance Quality on Electrical Motor Failures: A Hidden Markov Model Approach

Abstract

The reliability of induction motors, a critical component in industrial systems, is strongly influenced by maintenance practices, yet many industries still adopt inadequate or run-to-failure strategies. This study aims to investigate the underlying causes of motor failures by distinguishing between properly maintained and poorly maintained operational conditions. A probabilistic modeling approach based on a Hidden Markov Model (HMM) is employed to capture the relationship between observable failure types and hidden maintenance states. The model defines two hidden states maintained and poorly maintained and three observation categories: electrical, mechanical, and environmental faults. Using predefined transition and emission probabilities, the Forward algorithm is applied to evaluate the likelihood of observed failure sequences, while the Viterbi algorithm is utilized to determine the most probable sequence of hidden maintenance states. A Python-based implementation is developed to simulate and analyze failure patterns under different initial conditions. The results demonstrate that maintenance quality significantly affects the sequence and probability of motor failures, with poor maintenance conditions leading to a higher likelihood of rapid and compounded faults. Furthermore, variations in initial state probabilities produce notably different diagnostic outcomes, emphasizing the importance of accurate maintenance history data. The findings confirm that HMM is an effective tool for modeling and predicting failure behavior in induction motors. It is concluded that integrating predictive and preventive maintenance strategies with probabilistic models can enhance system reliability, reduce operational costs, and extend equipment lifespan.