Binary tree SVM based framework for mining fatigue induced damage attributes in complex lug joints

Research is being conducted in damage diagnosis and prognosis to develop state awareness models and residual useful life estimates of aerospace structures. This work describes a methodology using Support Vector Machines (SVMs), organized in a binary tree structure to classify the extent of a growing crack in lug joints. A lug joint is a common aerospace 'hotspot' where fatigue damage is highly probable. The test specimen was instrumented with surface mounted piezoelectric transducers and then subjected to fatigue load until failure. A Matching Pursuit Decomposition (MPD) algorithm was used to preprocess the sensor data and extract the input vectors used in classification. The results of this classification scheme show that this type of architecture works well for categorizing fatigue induced damage (crack) in a computationally efficient manner. However, due to the nature of the overlap of the collected data patterns, a classifier at each node in the binary tree is limited by the performance of the classifier that is higher up in the tree.