Unsupervised anomaly detection for Aircraft Condition Monitoring System

Anomaly detection is an important field for the anticipation of aircraft maintenance operations, working as an enabler of diagnostic and prognostic functions. A method has been implemented to detect abnormal data in Aircraft Condition Monitoring System (ACMS) records. Rather than using already known and usual detection triggers which are partial detectors and insensitive to new flight and system conditions, this method automatically extracts abnormal data points without requiring any a priori information about the system and its conditions. To accomplish this objective, we propose to combine a segmentation based and density clustering approaches for detecting and filtering anomalies. This method was applied on A340 ACMS data recordings. The detection logics associated with the new anomalies can be used as new detection conditions to be potentially implemented onboard, further extending legacy detection capabilities.