A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers

The objective of this paper is to demonstrate a novel signal processing for detection, identification and flaw sizing of structural damage using ultrasonic testing with Electromagnetic Acoustic Transducers (EMATs). Damage detection involves the recognition of a defect that exists within a structure. Damage location is the identification of the geometric position of the defect. Defect classification is the cluster of the damage type into multiple damage scenarios. In the absence of external interferences, a good measure of detectability of a flaw is its signal-to-noise ratio (SNR). Although the SNR depends on various parameters such as electronics used, material properties, e.g. homogeneity and damping, and flaw size, it can be improved using advanced signal processing. The main scientific novelties presented in this paper focus on filtering signal noise through advanced digital signal processing; incorporating wavelet transforms for image and signal representation enhancements; investigating multi-parametric analysis for noise identification and defect classification; studying attenuation curves properties for defect localisation improvement and flaw sizing and location algorithm development.