LSSVM with Fuzzy Pre-processing Model Based Aero Engine Data Mining Technology

Authors:
Xuhui Wang;Shengguo Huang;Li Cao;Dinghao Shi;Ping Shu
Affiliations:
College of civil aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016,;College of civil aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016,;College of civil aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016,;General Civil Aviation Administration of China, Center of Aviation Safety Technology, Aviation Safety Institute Technology Lab, Beijing, 100028,;General Civil Aviation Administration of China, Center of Aviation Safety Technology, Aviation Safety Institute Technology Lab, Beijing, 100028,
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

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Abstract

The operations of aircraft fleets typically result in large volumes of data collected during the execution of various operational and support processes.This paper reports on an Airlines-sponsored study conducted to research the applicability of data mining for processing engine data for fault diagnostics. The study focused on three aspects: (1) understanding the engine fault maintenance environment, and data collection system; (2) investigating engine fault diagnosis approaches with the purpose of identifying promising methods pertinent to aircraft engine management; and (3) defining a Support Vector Machines model with Fuzzy clustering to support the data mining work in aero engine fault detection. Results of analyses of maintenance data and flight data sets are presented. Architecture for mining engine data is also presented.