Reliability Analysis and predictive maintenance of Elevator System Using Hidden Markov Model

Abstract

Safety and operational efficiency in elevator systems are critical, our method leverages Hidden Markov Models (HMMs) to provide a comprehensive solution. We introduce an innovative approach to enhance the reliability analysis and predictive maintenance of elevator systems. It can predict the most probable sequence of hidden states based on new sensor data, offering real-time insights into the elevator system's health and enabling early issue detection. This approach not only enhances safety and reliability but also improves operational efficiency by allowing data-driven decision-making in elevator system maintenance. © 2024 IEEE.