MPCA-based Domain Adaptation for Transfer Learning in Ultrasonic Guided Waves

TL;DR

This paper introduces a novel MPCA-based transfer learning framework for ultrasonic guided wave damage localization, significantly improving domain adaptation and reducing errors across diverse materials and sensor configurations.

Contribution

The work presents a new transfer learning method combining MPCA and fine-tuning, enabling effective domain adaptation for UGW-based SHM without large datasets.

Findings

Substantial reduction in localization error compared to standard TL methods.

Effective domain alignment demonstrated across 12 diverse case studies.

Robust and data-efficient approach for real-time structural health monitoring.

Abstract

Ultrasonic Guided Waves (UGWs) represent a promising diagnostic tool for Structural Health Monitoring (SHM) in thin-walled structures, and their integration with machine learning (ML) algorithms is increasingly being adopted to enable real-time monitoring capabilities. However, the large-scale deployment of UGW-based ML methods is constrained by data scarcity and limited generalisation across different materials and sensor configurations. To address these limitations, this work proposes a novel transfer learning (TL) framework based on Multilinear Principal Component Analysis (MPCA). First, a Convolutional Neural Network (CNN) for regression is trained to perform damage localisation for a plated structure. Then, MPCA and fine-tuning are combined to have the CNN work for a different plate. By jointly applying MPCA to the source and target domains, the method extracts shared latent features, enabling effective domain adaptation without requiring prior assumptions about dimensionality. Following MPCA, fine-tuning enables adapting the pre-trained CNN to a new domain without the need for a large training dataset. The proposed MPCA-based TL method was tested against 12 case studies involving different composite materials and sensor arrays. Statistical metrics were used to assess domains alignment both before and after MPCA, and the results demonstrate a substantial reduction in localisation error compared to standard TL techniques. Hence, the proposed approach emerges as a robust, data-efficient, and statistically based TL framework for UGW-based SHM.