摘要：AbstractToothed belt drives are used in manifold automation applications. But only if the belt tension is properly adjusted, optimal working conditions are ensured. A loss of efficiency or even breakdowns might be the consequences otherwise. For this reason, tension monitoring reduces operation costs and may prevent failures. In order to meet industrial requirements, the monitoring is supposed to rely on standard sensor data. From this data, features are extracted in time and frequency domain which are passed on to a random forest. For further improvement, a segmentation of the frequency spectrum is performed beforehand. In this way, interval-based spectral features can be extracted to capture small distinctive parts in the frequency domain. For this purpose, two different segmentation procedures are compared in a random forest regression. A belt drive powered by a 1.9 kW synchronous servomotor is used to evaluate the proposed approaches in two different industrial scenarios. The experimental results show that both segmentation methods enhance the performance of a tree-based regression and offer a reliable tension prediction.
关键词：KeywordsFault detectiondiagnosisMachine learningIndustrial production systemsTime series modelingSegmentation
