Condition Based Maintenance of Low Speed Rolling Element Bearings using Hidden Markov Model

Abstract

This paper presents an integrated hidden Markov model (HMM) approach to undertake fault diagnosis and maintenance planning for low-speed roller element bearings in a conveyor system. The components studied are relatively long-life components for which run-to-failure data is not available. Furthermore, the large number of these components in a conveyor system makes the individual monitoring of each bearing impractical. In this paper, HMM is employed to overcome both these challenges. For fault diagnosis, a number of bearings varying in age and usage were extracted from the system and tested to develop a baseline HMM model. This data was then used to calculate likelihood probabilities, which were subsequently used to determine the health state of an unknown bearing. For maintenance planning, experimentally determined thresholds from faulty bearings were used in conjunction with simulated degradation paths to parametrize a HMM. This HMM is then used to determine the state duration statistics and subsequently the calculation of residual useful life (RUL) based on bearing vibration data. The RUL distribution is then used for maintenance planning by optimizing the expected cost rate and the results so obtained are compared with the results obtained from a traditional age based replacement policy.