Physics verification and validation for transferring data between bearings

Abstract

Bearings are critical components for transferring load and motion between subsystems with reduced friction for rotational equipment. Manufacturers implement condition monitoring technology to prevent failures of bearings by using different data acquisition methods, such as vibration, acoustics, temperature, motor current, and ultrasonic sensing, to monitor and predict changes that could indicate early equipment degradation. However, the data quality and availability varies depending on the application, and low data availability from physical environments can lead to poorly trained models. This paper explores how to transfer data and information about failure propensity between bearings of different sizes using a combined physics and data-driven approach. Though the approach is exemplified with roller bearings, the method is extensible to other types of failures in different-sized equipment. Data are generated using an experimental test stand measuring failure of one component, with the intent to scale findings to represent a real-world system; findings are verified with a physics-based model. Data from three different bearing sizes, i.e. 6205, 6206, and 6207, are used to train the algorithms to identify similarity among the sets. Classifiers trained with the raw data provide over 90% accuracy, leading to the conclusion that the data classes are separable based on bearing size. 6205 and 6207 data were scaled to simulate 6206 and tested to see if a classifier could differentiate between the true 6206 bearing data and the simulated bearing data derived from the 6205 and 6207 data. In the simulated data, the classifier accuracy for each algorithm dropped below 90% to as low as 50% (Naive Bayes case). The lower accuracy implied a greater overlap in the vibration features, increasing the data similarity between the different bearing sizes. Future work will further investigate defining dimensionless numbers as scaling parameters for bearing data.