Abstract
Structural health monitoring (SHM) is a long standing problem in the area of aircraft structures. Recently data-driven SHM technique using machine learning (ML) algorithms are being explored extensively. The benefits of ML-based SHM methodologies include capabilities to handle structures of higher complexities, varying environmental and operational conditions, and potential for online monitoring. In this work, artificial neural network (ANN) is implemented for damage detection using Lamb response of thin aluminium plate. The Lamb wave responses are obtained both experimentally and through finite element (FE) simulation using ABAQUS. The efficiency of ANN model strongly depends on proper selection of input features from the Lamb wave responses. Here, an automated feature extraction tool, namely, Time Series Feature Extraction on basis of Scalable Hypothesis tests (Tsfresh) is used. Next, feature selection is performed, which involves elimination of irrelevant or less important features. Tsfresh also automates this process by comparing the features against output variables. The proposed ANN model with the automated feature selection predicts the presence of damage with 100% accuracy.
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Prajapati, K.K., Rai, A. & Mitra, M. Lamb Wave-Based Damage Detection Using Artificial Neural Network and Automated Feature Extraction. Trans Indian Natl. Acad. Eng. 7, 1009–1016 (2022). https://doi.org/10.1007/s41403-022-00342-2
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DOI: https://doi.org/10.1007/s41403-022-00342-2