Title :
Unsupervised anomaly detection for Aircraft Condition Monitoring System
Author :
Dani, Mohamed Cherif ; Freixo, Cassiano ; Jollois, Francois-Xavier ; Nadif, Mohamed
Author_Institution :
Airbus, LIPADE Descartes Univ., France
Abstract :
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.
Keywords :
aerospace computing; aircraft; condition monitoring; maintenance engineering; mechanical engineering computing; pattern clustering; A340 ACMS data recordings; aircraft condition monitoring system; aircraft maintenance operations; density clustering approaches; detection triggers; diagnostic functions; flight conditions; legacy detection capabilities; prognostic functions; segmentation based approaches; system conditions; unsupervised anomaly detection; Aircraft; Approximation methods; Clustering algorithms; Maintenance engineering; Monitoring; Sensors; Time series analysis;
Conference_Titel :
Aerospace Conference, 2015 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
978-1-4799-5379-0
DOI :
10.1109/AERO.2015.7119138