Convolutional Neural Networks for Ultrasonic Guided Wave-Based Structural Damage Detection and Localisation

Among the many methods proposed in the literature to perform structural health monitoring (SHM) of thin-walled structures, two of them appear to be particularly promising and complementary. On the one hand, integrating Machine Learning techniques into this field seems a remarkable solution, since these methods have been shown to be effective in recognising usually hard-to-detect recurring patterns in the measured signals related to the presence of damages in structures, thus improving the diagnostic performances of SHM frameworks. In particular, in the past years, Deep Learning algorithms have gained much importance in this field due to their capability of processing high-dimensional inputs (such as images), thus making it possible to automatically identify onsetting structural damages. On the other hand, ultrasonic guided wave-based approaches are commonly adopted to assess the structural integrity of plate-like structures and pipelines. These approaches, coupled with tomographic algorithms, typically allow performing damage detection and localisation with satisfactory results. However, such reconstruction algorithms are significantly sensors layout-dependent and, as such, they come with some still unsolved issues, leading, for example, to artifacts creation and unsatisfactory tomographic damage localisation performances in case of unevenly distributed network of sensors or when few sensors are installed on the structure. In this work, convolutional neural networks (CNNs) and ultrasonic guided waves are combined into a unique framework, which leverages on the advantages of the two methods to perform damage detection and localisation in plate-like structures. Guided waves are excited and sensed by a network of sensors permanently installed on the structure. The information acquired is then converted into grayscale image as is, without performing any prior feature extraction procedure, which is further analysed by a set of CNNs. First, a classifier is employed to perform damage detection. In case damage is identified, the grayscale image is then analysed by two regression CNNs to localise the damage. The framework is tested using experimentally validated numerical simulations of guided waves propagating in a metallic plate available in the literature.