Online tribology ball bearing fault detection and identification

B. Ling; M. M. Khonsari

doi:10.1117/12.719416

3 May 2007 Online tribology ball bearing fault detection and identification

B. Ling, M. M. Khonsari

Proceedings Volume 6555, Sensors and Systems for Space Applications; 65551A (2007) https://doi.org/10.1117/12.719416
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

B. Ling and M. M. Khonsari "Online tribology ball bearing fault detection and identification", Proc. SPIE 6555, Sensors and Systems for Space Applications, 65551A (3 May 2007); https://doi.org/10.1117/12.719416

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