A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals

This paper introduces a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. First features are extracted from amplitude demodulated vibration signals obtained from both normal and faulty bearings. The features are based on the reflection coefficients of the polynomial transfer function of the autoregressive model of the vibration signal. These features are then used to train HMMs to represent various bearing conditions. The technique allows for online detection of faults by monitoring the probabilities of the pre-trained HMM for the normal case. It also allows for the diagnosis of the fault by the HMM that gives the highest probability. The new scheme was tested with experimental data collected from drive end ball bearing of an induction motor (Reliance Electric 2HP IQPreAlert) driven mechanical system.