Development of Autoencoder-Based Status Diagnosis Method for Ball Bearing Tribology Status Monitoring

Ball bearings are widely used in rotating components and definitely influence the operation quality of machines. And their faults are one of the main reasons that make machines break down and this problem should be investigated. Thus, this study develops an effective and flexible diagnosis method for ball bearings. By setting up a rotor bearing-experiment platform and pre-designing failures on bearings or rotating shaft to simulate the service of bearings. Sensors hired are two accelerometers, a microphone, an acoustic emission detector, and a thermal couple. Various statistical methods are used for data reduction and to extract features. Through systematic analysis, it is possible to find the most sensitive features. Those indexes are then fed into autoencorder, which is an unsupervised machine learning scheme, for training the collected data to predict the possible bearing failure type and status. For ease of visualization, the results are mapped into a three-dimensional space for examining the performance in failure diagnosis. The investigation results show that the hired machine learning method performs well with appropriate feature indexes. Finally, specific diagnosis models are created for each corresponding bearing failure condition and a novel whole diagnosis process is proposed to integrate all these models for counting possible multiple causes of failure. This proposed diagnosis flow should be able to significantly improve the prediction accuracy on the reliabilities of rotating machines and could be promoted to other related applications.