Towards Adaptive Anomaly Detection and Root Cause Analysis by Automated Extraction of Knowledge from Risk Analyses

Bram Steenwinckel , , Dieter De Paepe , Olivier Janssens , Sander Vanden Hautte , , Filip De Turck , Sofie Van Hoecke , Femke Ongenae
In Proceedings of the 9th International Semantic Sensor Networks Workshop (2018)

Sensors, inside internet-connected devices, analyse the environment and monitor possible unwanted behaviour or the malfunctioning of the system. Current risk analysis tools, such as Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA), provide prior information on these faults together expert-driven insights of the system. Many people are involved in this risk analyses process, resulting in disambiguations and incompleteness. Ontologies could resolve this issue by providing a uniform structure for the failures and their causes. However, domain experts are not always ontology experts, resulting in a lot of human effort to keep the ontologies up to date. In this paper, automated mappings from the FMEA data to a domain-specific ontology and the generation of rules from a constructed FTA were researched to annotate and reason on sensor observations semantically and provide some firststeps towards automated, expert-driven fault detection. The approach is demonstrated with a use case to investigate the possible failures and causes of reduced passenger comfort levels inside a train