Wavelet-based outlier analysis for guided wave structural monitoring: Application to multi-wire strands

In this paper we describe a method based on outlier analysis and the wavelet transform for structural damage detection based on guided ultrasonic waves. The basic idea is to de-noise and compress the ultrasonic signals by the discrete wavelet transform and use the relevant wavelet coefficients to construct a unidimensional or multidimensional damage index. The damage index is then fed to an outlier analysis to detect anomalies that are representative of structural defects. By extracting the essential information from the ultrasonic signals, the dimension of the damage index is kept at a minimum, as desirable for continuous structural monitoring. The general framework is applied to the detection of notch-like defects in a seven-wire strand by using built-in magnetostrictive devices for ultrasound transduction. Random noise is digitally added to the raw ultrasonic measurements to create statistical populations of the baseline (undamaged) conditions and the damaged conditions. This application demonstrates the effectiveness of the multidimensional analysis compared to the unidimensional analysis, while keeping the number of features as low as four.