Selection of instances in Condition Based Monitoring: the case of aircraft engines

Abstract

Condition based maintenance (CBM) is based on analysis and data collection monitored by sensors on the aircraft. The knowledge discovery, about the performance of different parameters, by using these data will provide new ways of diagnosing and predicting the state of aircraft engines. However, a single flight produces a huge amount of data that characterize aircraft engine behaviour. The use of algorithms for the simultaneous processing of these data is a difficult and sometimes impossible task. The objective of this work is to choose the best way to select instances for a sample. There should be no loss of relevant information in the sample to identify the state of the engine. We use five methods to select the sample and through clustering techniques and sensibility analysis we choose the best way to select the sample.