Extracting Knowledge using Machine Learning for Anomaly Detection and Root-Cause Diagnosis

Root-cause diagnosis techniques, such as consistency-based and abductive diagnosis, offer essential support in explaining symptoms in a cyber-physical system. Developing and maintaining the required (detailed or abstract) models can be a serious challenge. Related issues include the complexity of the required knowledge and the dynamic changes we see in a system over its life cycle. This raises the question regarding strategies and the feasibility of utilizing unsupervised machine learning to learn diagnostic system models based on available time series data in order to address this challenge. This paper presents the novel methodology Discret2DeepDive for automated learning of diagnostic system models for root-cause diagnosis, focussing on the use of automata for state mapping over time and explores advancements related to the handling of dynamic time series data. These advancements are incorporated into both the discretization process and the generation of residuals. The findings demonstrate a notable enhancement in discretizing time series data into modes and residual generation for anomaly detection in sequential data, thereby providing a substantial value for diagnosing faults.