Autoencoder and Incremental Clustering-Enabled Anomaly Detection

Abstract

Many machine-learning-enabled approaches towards anomaly detection depend on the availability of vast training data. Our data are formed from power readings of cycles from domestic appliances, such as dishwashers or washing machines, and contain no known examples of anomalous behaviour. Moreover, we are limited to the machine’s voltage, amperage, and current readings, drawn from a retrofitted power outlet in 60-s samples. No rich sensor data or previous insights are available as a training basis, limiting our ability to leverage the existing work. We design a system to monitor the behaviour of electrical appliances. This system requires special consideration as different power cycles from the same machine can exhibit different behaviours, and it accounts for this by clustering unseen cycle patterns into siloed training datasets and corresponding learned parameters. They are then passed in real-time to an autoencoder ensemble for reconstruction-based anomaly detection, using the error in reconstruction as a means to flag anomalous points in time. The system correctly identifies and trains appropriate cycle clusters of data streams on a real-world machine dataset injected with stochastic, proportionate anomalies.