Damage location in an isotropic plate using a vector of novelty indices

Abstract

This paper presents a novel approach to detecting and localising structural defects based on a novelty detection method, outlier analysis (OA), and a multi-layer perceptron (MLP) neural network. In order to assess the effectiveness of the approach, a thin rectangular plate with isotropic behaviour was evaluated experimentally. The scope of this present work also comprises an investigation of the scattering effect of an ultrasonic guided wave on the tested plate under both damaged and undamaged conditions. The wave propagation is sequentially transmitted and captured by 8 PZT patches bonded on the plate, forming a sensor network on the tested isotropic rectangular structure. An in-house 8 channel multiplexer is incorporated in this small scale and low-cost ‘structural health monitoring’ (SHM) system to effectively swap the PZTs role from sensor to actuator and vice-versa. The ‘real-time damage demonstrator’ software is primarily developed to acquire and store the waveform responses. These sets of scattering waveform responses representing normal and damage conditions are transformed into a set of novelty indices that ultimately determine the true conditions of the tested structure. The acquired novelty indices representing the available sensor paths are used as the inputs for the neural network incorporating the MLP architecture to compute and predict the damage location in the x and y location on the tested isotopic plate.