Unsupervised learning framework for temperature compensated damage identification and localization in ultrasonic guided wave SHM with transfer learning

Abstract

Damage localization algorithms for ultrasonic guided wave-based structural health monitoring (GW-SHM) typically utilize manually-defined features and supervised machine learning on data collected under various conditions. This scheme has limitations that affect prediction accuracy in practical settings when the model encounters data with a distribution different from that used for training, especially due to variation in environmental factors (e.g., temperature) and types of damages. While deep learning based models that overcome these limitations have been reported in literature, they typically comprise of millions of trainable parameters. As an alternative, we propose an unsupervised approach for temperature-compensated damage identification and localization in GW-SHM systems based on transferring learning from a convolutional auto encoder (TL-CAE). Remarkably, without using signals corresponding to the damages during training (unsupervised), our method demonstrates more accurate damage detection and localization as well as robustness to temperature variations than supervised approaches reported on the publicly available Open Guided Waves (OGW) dataset. Additionally, we have demonstrated reduction in number of trainable parameters using transfer learning (TL) to leverage similarities between time-series among various sensor paths. It is also worth noting that the proposed framework uses raw time-domain signals, without any pre-processing or knowledge of material properties. It should therefore be scalable and adaptable for other materials, structures, damages, and temperature ranges, should more data become available in the future. We present an extensive parametric study to demonstrate feasibility of the proposed method.