Towards Semi-Supervised Classification of Event Streams via Denoising Autoencoders

Abstract

In predictive maintenance, one may face a scenario where a series of anomalous events is indicative of an impending fault. While each of them by itself would not be sufficient for setting off an alarm, their collective occurrence is. However, supervised training of recognizers for these anomalous events is difficult. The number of occurrences of such faults is generally low, and the derived labels are unreliable because they apply to the entire sequence-a so-called mission-and not the individual events. In this paper, we propose an approach for tackling such problems via unsupervised training of autoencoders on data of normal events. Individual anomalies are recognized via the reconstruction error. Missions are then classified via a threshold-based approach on the ensemble of anomaly ratios. Our method handles artificially generated data well and is robust against noisy data. Its main advantage is a low level of supervision, since all the parameters can be extracted experimentally with little knowledge about the ground truth in the data.