Online Anomaly Detection on the Edge

Abstract: The society of today relies a lot on the industry and the automation of factory tasks is more prevalent than ever before. However, the machines taking on these tasks require maintenance to continue operating. This maintenance is typically given periodically and can be expensive while sometimes requiring expert knowledge. Thus it would be very beneficial if one could predict when a machine needs maintenance and only employ maintenance as necessary. One method to predict when maintenance is necessary is to collect sensor data from a machine and analyse it for anomalies. Anomalies are usually an indicator of unexpected behaviour and can therefore show when a machine needs maintenance. Due to concerns like privacy and security, it is often not allowed for the data to leave the local system. Hence it is necessary to perform this kind of anomaly detection in an online manner and in an edge environment. This environment imposes limitations on hardware and computational ability. In this thesis we consider four machine learning anomaly detection methods that can learn and detect anomalies in this kind of environment. These methods are LoOP, iForestASD, KitNet and xStream. We first evaluate the four anomaly detectors on the Skoltech Anomaly Benchmark using their suggested metrics as well as the Receiver Operating Characteristic curves. We also perform further evaluation on two data sets provided by the company Gebhardt. The experimental results are promising and indicate that the considered methods perform well at the task of anomaly detection. We finally propose some avenues for future work, such as implementing a dynamically changing anomaly threshold.