Prognostics of aircraft bleed valves using a SVM classification algorithm

Non planned maintenance in aircraft systems is usually associated with high costs. It is believed that at least part of these costs can be avoided if adequate system prognosis programs are used. The aim of these programs is to evaluate the current state of an aircraft component based on the available system data (e.g., flight and maintenance data) and to estimate the future performance and the remaining useful life of this component. Several algorithms can be used for this purpose. This paper proposes a method of performing prognostics on aircraft component based a binary classification Support Vector Machine (SVM) Classification algorithm. The algorithm is used to classify each flight according to a fault pattern which the algorithm was trained to recognize. Flight data and maintenance logs were used to generate the training and testing datasets. In this case study, for each flight, a number of characteristics were extracted from parameters of the aircraft Air Management System. From the classification results a degradation index is created to serve as an aid to better plan the aircraft maintenance. An advantage of the proposed method is that it does not require a deep knowledge on the system. Furthermore, it does not need a large amount of input-output samples to be trained. The method should be easily extended to other aircraft systems as long as enough flight data and maintenance logs are available.