Development of a bearing test-bed for acquiring data for robust and transferable machine learning

Developing test-beds to test products and procedures and gain further insights is a common approach in science and industry. During the testing, data is often recorded to provide further insights to the analysts. Since the rise of machine learning and data-driven approaches, these test-beds often get over-instrumented to record as much data as possible. After that, the data is analyzed, and tailored algorithms are applied to achieve a machine learning model with high accuracy. However, many of these models later fail when applied in the real world because they lose their validity due to dataset or domain shift. This means that certain cross-influences were not, or not in their complete range, covered within the recorded data. In this contribution, a test-bed for cylindrical roller bearings has been developed where multiple cross-influences can be varied. It is designed for subsequent leave-one-group-out cross-validation to evaluate the robustness and transferability of machine learning models. Noteworthy features of the test-bed are the possibility of changing the position of the bearing in the test-bed without disassembling it from the shaft (perhaps causing unintentional damages) and that each bearing is measured in its undamaged condition as well before damaging it. In a first measurement campaign, three experiments had been carried out with an automated machine learning toolbox to evaluate the performance of the test-bed design.