Abstract Summary
An autonomous Structural Health Monitoring (SHM) strategy is presented for the online identification and classification of different types of Acoustic Emission (AE) sources in composite structures. Towards this, a Convolutional Neural Network based deep learning network is prepared for the automatic detection and characterization of pencil-lead break damage (PLBD)- resembling artificial debonding generation, low-speed tool-drop (TD) and steel ball-drop (BD) impacts on a laboratory-based carbon-fibre reinforced composite structure. The proposed deep network uses the AE signals corresponding to a series of PLBD, TD and BD events on different predefined zones of the targeted composite structure. In the process, the registered AE signals time domain-domain are converted to time-frequency scalogram by performing the continuous wavelet transform. These scalogram images are then resized to reduce the computational effort. The classification results show high accuracy in the training, validation, and testing of the network. The study was further extended for the AE signals under environmental impacts. The results show good accuracy under variable environmental conditions. It is envisaged that this deep learning based autonomous SHM strategy can be translated for the real-time monitoring of several other structural components under variable operating conditions and applications.
