Online tribology ball bearing fault detection and identification

Abstract

We present a feasibility analysis for the development of an online ball bearing fault detection and identification system. This system can effectively identify various fault stages related to the evolution of friction within the contact in the coated ball bearings. Data are collected from laboratory experiments involving forces, torque and acceleration sensors. To detect the ball bearing faulty stages, we have developed a new bispectrum and entropy analysis methods to capture the faulty transient signals embedded in the measurements. Test results have shown that these methods can detect the small abnormal transient signals associated with the friction evolution. To identify the fault stages, we have further developed a set of stochastic models using hidden Markov model (HMM). Instead of using the discrete sequences, our HMM models can incorporate the feature vectors modeled as Gaussian mixtures. To facilitate online fault identification, we build an HMM model for each fault stage. At each evaluation time, all HMM models are evaluated and the final detection is refined based on individual detections. Test results using laboratory experiment data have shown that our system can identify coated ball bearing faults in near real-time.

Online coated ball bearing health monitoring using degree of randomness and Hidden Markov Model

“…A new bispectrum and entropy analysis method [19] has been developed to extract and classify the temporal transient features from bearing friction measurements and bearing faults via Gaussian Mixture Hidden Markov Model (GM-HMM). Here we further expand our previous work [19] and focus on non-invasive measurement such as vibration signals for coated bearing monitoring, thus the method is intended for use in existing instrumented thrust ball bearings whose races are coated with MoS 2 . The ball bearings are set in oscillatory motion under a specified amplitude and frequency and their behavior is monitored in near real-time.…”