To avoid sudden motor failures, which might lead to unexpected downtimes of entire production plants, predictive machinery maintenance is of great importance. Since bearingsare the only connection between rotor and stator and for thisreason, among others, more than 40 percent of all motor failures are reasoned by bearing defects, bearing defect detectionis of particular importance for predictive maintenance of motor failures. In the past, mainly external vibration sensorswere used for fault detection. However, in recent years, therehas been increasing research into using phase currents forbearing damage detection, which has the advantage of savingadditional sensors and thus costs. The practical use of phasecurrents is justified by the fact that bearing damage is accompanied by eccentricities between the rotor and stator, whichaffect the magnetic flux and thus also the phase currents. Forthese reasons, we focus on bearing damage detection usingphase currents.We show in the paper that variations of the rotational shaftfrequency, torque loads and radial forces applied to the bearings have a strong influence of the applicability of a defectdetection model trained with phase current data. This means,a defect detection model, which was trained with labeled datacollected under a fixed operating condition is not able to makereliable classifications if the operating conditions differ fromthe training conditions. This is due to discrepancies in feature distributions between the different operating conditions.However, it is of utmost importance for practical applicability that a fault detection model is able to make reliable classifications under a variety of previously unknown operating conditions.To solve this problem, we present a deep learning approachfor diminishing the influence of different operating conditionsby utilizing unsupervised domain adaptation. As input datafor the neural network, we propose to extract hand-craftedfeatures, both from time- and frequency domain, from thephase current signals. This greatly reduces training time andresource utilization compared to training a neural network asfeature extractor on the raw time series sensor signals. Thebasic idea is then to train a neural network with labeled datafrom the source domain, i.e. data of one defined operatingcondition, and at the same time consider unlabeled data ofthe new, previously unknown, target operating condition inorder to achieve higher classification accuracies for this operating condition as well. Thus, labeled data is only necessaryfrom the source domain to train the Softmax classificationlayer of the deep neural network. As the features computedfrom the differing working conditions are the same, domaindiscrepancy can be reduced in an unsupervised manner byweight sharing and formulating a specific loss term (we useMulti-Layer Maximum Mean Discrepancy) that captures thediscrepancy between the different operating conditions. Byapplying this additional loss term to the backpropagation process, discrepancies between the feature means of source- andtarget domain are reduced iteratively.The effectiveness of the proposed method is evaluated with abenchmark dataset from the University of Paderborn, which iswell known in the bearing damage community. The proposedfeature based deep unsupervised domain adaptation methodsignificantly increases classification accuracies for all transfer scenarios. With the best of our knowledge, it is the firsttime one deals with phase current based domain adaptationon statistical fault-features.
