voraus-AD

voraus-AD contains machine data of a collaborative robot, which moves a can by performing an industrial pick-and-place task. The samples consist of time series of machine data, each recorded over one pick-and-place operation. As usual in anomaly detection, the training set contains only normal data, which includes regular samples without anomalies. The test set contains both, normal data and anomalies, including 12 diverse anomaly types. In order to create a realistic scenario, we have divided the normal data into training and test data as follows: Up to a certain period of time, only training data including 948 samples was recorded. Subsequently, recordings of anomalies (755 samples) and normal data (419 samples) for the test set were taken alternately. This simulates a real application where training data would be recorded first in the same way to train the model before the test case occurs. To exclude temperature effects, we let robots warm up for half an hour before each recording.