Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data

Sensors

Matthias Kahr¹  Hubert Brückl¹  Gabor Kovács¹  Markus Loinig² 

[1] Department for Integrated Sensor Systems, University for Continuing Education Krems, 2700 Wiener Neustadt, Austria;Senzoro GmbH, 1030 Wien, Austria;

关键词: condition monitoring;    roller bearing;    fault detection;    machine learning;    wavelet transform;    simulated training data;   

DOI  :  10.3390/s22072490

来源: DOAJ

【 摘 要 】

Rolling element bearing faults significantly contribute to overall machine failures, which demand different strategies for condition monitoring and failure detection. Recent advancements in machine learning even further expedite the quest to improve accuracy in fault detection for economic purposes by minimizing scheduled maintenance. Challenging tasks, such as the gathering of high quality data to explicitly train an algorithm, still persist and are limited in terms of the availability of historical data. In addition, failure data from measurements are typically valid only for the particular machinery components and their settings. In this study, 3D multi-body simulations of a roller bearing with different faults have been conducted to create a variety of synthetic training data for a deep learning convolutional neural network (CNN) and, hence, to address these challenges. The vibration data from the simulation are superimposed with noise collected from the measurement of a healthy bearing and are subsequently converted into a 2D image via wavelet transformation before being fed into the CNN for training. Measurements of damaged bearings are used to validate the algorithm’s performance.

【 授权许可】

Unknown