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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.

The qualities of machined products are largely depended on the status of machines in various aspects. Thus, appropriate condition monitoring would be essential for both quality control and longevity assessment. Recently, with the advance in artificial intelligence and computational power, status monitoring and prognosis based on data driven approach becomes more practical. However, unlike machine vision and image processing, where data types are fixed and the performance index has already well defined, sensor selection and index for machine tools are versatile and not standardized at this moment. Without supporting of appropriate domain knowledge for selecting appropriate sensors and adequate performance index, pure data driven approach might suffer from unsatisfied prediction accuracy and needing of excessive training data, as well as the possibility of misjudgment. This would be a key obstacle for promoting data driven based prognosis in general intelligent manufacturing field. In this work, the status monitoring and prediction of a cutter wear problem is investigated to address the above concerns and to demonstrate the possible solutions by hiring a 5-axis machine center equipped with milling cutters of different wear levels. Transducers including accelerometers, microphones, current transformer, and acoustic emission sensors are mounted on the spindle, fixture, and nearby structures to monitor the milling process. The collected data are processed to extract various signatures and the key dominated indexes are identified. Finally, three multilayer perception (MLP) artificial neural network models are established. These models trained by different input features are compared to examine the influence of selected sensors and indexes on the prediction accuracy. The results show that with appropriate sensors and signatures, even with less amount of experimental data, the model can indeed achieve a better prediction. Therefore, a proper selection of indexes guided by physical knowledge based experiment or theoretical investigation would be critical.