Print Email Facebook Twitter Anomaly Detection in ACMS Data for Predictive Maintenance at KLM Engineering & Maintenance Title Anomaly Detection in ACMS Data for Predictive Maintenance at KLM Engineering & Maintenance Author Lion, W.F. Contributor Verhagen, W.J.C. (mentor) Van Kesteren, R.F.A. (mentor) Faculty Aerospace Engineering Department Control & Operations Programme Air Traffic Operations Date 2016-04-08 Abstract This thesis investigates the opportunity to use the massive amounts of data coming from modern aircraft to predict maintenance tasks. The goal is to predict defects in such a fashion as to prevent the actual failure from occurring. This results in fewer delays and less maintenance costs. The aircraft data, ACMS data, is analysed using three anomaly detection modules. The three methods are correlation based, subsequence based and noise based anomaly detection. Results show very promising and have resulted in the preventive removal of 17 components within the KLM B747 fleet to this date. Subject ACMSPredictiveKLMAnomaly DetectionSubsequenceMaintenance To reference this document use: http://resolver.tudelft.nl/uuid:b55c9a28-c034-4ca6-9360-294bc8938d72 Embargo date 2021-04-08 Part of collection Student theses Document type master thesis Rights (c) 2016 Lion, W.F. Files PDF Thesis_Wouter_Lion_Repository.pdf 11.7 MB Close viewer /islandora/object/uuid:b55c9a28-c034-4ca6-9360-294bc8938d72/datastream/OBJ/view