Aircraft bleed valve fault classification using support vector machines and classification trees

This work consisted in developing a data driven solution to identify and isolate faults on aircraft bleed valves using supervised machine learning algorithms. A physical computer model of the valve with noisy observations and variable environmental conditions was used to simulate a set of operationing conditions submitted to three different failures modes: leakage between the valve chambers, degraded return spring and excessive piston friction. Features extracted from the model simulations were used to train and test a Support Vector Machine (SVM) as well as a Classification Tree. Different SVM Kernels and tree pruning limits were considered at the training step. Both solutions were evaluated at the testing step by evaluating their accuracy and precision. The results evidenced that both algorithms could identify and classify the faults with a slightly better performance of the SVM. Also, results evidenced that this application could automate the fault isolation process of these types of valves. This work evidences the importance of the proper definition of the classification algorithms as well as their metaparameters in order to obtain the most suitable solution for fault isolation developments.