Self-supervised Multi-stage Estimation of Remaining Useful Life for Electric Drive Units

The use of pedelecs as a mobility solution has increased considerably in recent years. One of their main components, the drive unit, consists of several mechanical elements such as bearings and gears, which deteriorate over time and, thus, increasing the probability of a major failure. This work introduces a data-based approach for monitoring the drive unit’s condition and forecasting failures, in order to ensure the reliability of the system. A trustworthy prediction of anomalies requires a vast dataset to train and test the selected algorithms. To this end, we make use of a database consisting of data collected for the past couple of years during endurance tests of almost one hundred drive units. The collected database allow us to test machine learning approaches under more realistic prognosis applications in comparison to existing published works, which brings diverse challenges such as unlabeled and unbalaced data. The focus of the present approach is the data preprocessing through different stages, such as data labeling and undersampling, to reduce the negative impact of the problematics that involve this database. Afterwards, a Gaussian process for regression is trained with these preprocessed data to predict the remaining useful life of the drive unit. The experimental study shows that by performing these preprocessing stages, an accurate estimation of the time to failure of the drive unit can be achieved.