Real-time industrial systems anomaly detection with on-edge Tiny Machine Learning

Embedded system such as microcontrollers has become more powerful and cheaper during the past couple of years. This has led to more and more development of on-edge applications, one of which is anomaly detection using machine learning. This thesis investigates the ability to implement, deploy and run the unsupervised anomaly detection algorithm called Isolation Forest, and its modified version Mondrian Isolation Forest on a microcontroller. Both algorithms were successfully implemented and deployed. The regular Isolation Forest algorithm resulted in being able to function as an anomaly detection algorithm by using both data sets and streaming data. However, the Mondrian Isolation Forest was too resource hungry to be able to function as a proper anomaly detection application