Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Abstract

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.

Evaluation of machine learning techniques for structural health monitoring using ultrasonic guided waves under varying temperature conditions

“…In Ref. 34, Schnur et al proposed an approach based on a ML toolbox for condition monitoring that combines several complementary algorithms and focuses on the physical interpretability of the results, and examined the dataset that we also used for our study. Several feature extraction methods, including PCA, were used to reduce dimensionality, and then, feature selection methods were applied to choose the best combination of reduced features and perform classification according to the structural condition.…”

A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures

“…Machine learning (ML) is a data-driven decision-making method that automatically analyzes patterns from data and uses the patterns to make predictions about unknown data [ 119 ]. ML has a superior ability to identify and classify patterns in datasets and can be used as an extension of traditional damage detection techniques.…”

Interpretable and Explainable Machine Learning for Ultrasonic Defect Sizing

“…Another published approach is to use well-known dimensionality reduction methods such as principal component analysis (PCA) to reduce the complexity of input data. This has been used with a support vector machine (SVM) to detect damage in carbon fiber reinforced polymer plate using ultrasonic guided wave data [27].…”