Damage Detection in Pipes under Changing Environmental Conditions Using Embedded Piezoelectric Transducers and Pattern Recognition Techniques

This paper presents the preliminary results of a research project that investigates the feasibility of continuous monitoring techniques using piezoelectric transducers (PZTs) permanently installed on steel pipes. The ultrasonic waves generated by PZTs are multimodal and dispersive. Therefore, it is difficult to detect changes created by the presence of damage, and it is even more difficult to differentiate changes produced by damage from benign changes produced by variation in environmental and operational conditions. In this paper, the results are reported of applying pattern recognition techniques to detect a mass scatterer (a proxy for damage) under ambient variations primarily due to varying internal pressure of a pipe. Using wavelet methods, 303 features are extracted, and adaptive boosting, modified adaptive boosting, and support vector machines for damage detection are employed. The performances of the three classifiers are evaluated over 41 trials with different combinations of training and testing data, resulting in the average accuracies of 85, 89, and 94%, respectively. Finally, the effectiveness of wavelet processing and features selected are discussed.

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