Non-destructive diagnostic of aircraft engine blades by Fuzzy Decision Tree

Abstract

A new algorithm for non-destructive diagnosis based on signal classification is proposed. This algorithm permits classifying objects with different properties depending on signals characterizing the objects. In this paper, the algorithm is applied to diagnose blades of aircraft engine gas turbine according to vibration signal after a non-destructive shock excitation and to classify them as defective and faultless. Just like other signal classification algorithms, this algorithm consists of two steps that are signal preliminary transformation and classification. However, these steps are modified in the proposed algorithm. Unlike other algorithms, the fuzzy classifier is used in the classification step. The change of the classifier type causes modification of the preliminary transformation step because the attributes for the classification must be fuzzy. Therefore, fuzzification procedure is added into the step of preliminary transformation. The fuzzy classifier for the problem of aircraft engine blades diagnosis is ordered Fuzzy Decision Tree (oFDT) that is inducted by estimation of Cumulative Mutual Information. This induction has good efficiency for a small set of initial data. The accuracy of the classification of defective and faultless blades for the proposed algorithm is 98.5%, and oFDT for this classification is inducted based on 32 signals only. The comparison with other classification algorithms shows that oFDT based algorithm considered in this paper gives the best result for this problem.