Contrastive Metric Learning Loss-Enhanced Multi-Layer Perceptron for Sequentially Appearing Clusters in Acoustic Emission Data Streams
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Abstract
Conventional structural health monitoring methods for interpreting unlabeled acoustic emission (AE) data typically rely on generic clustering approaches. This work introduces a novel approach for analyzing sequential and temporal acoustic emission (AE) data streams by enhancing a Multi-Layer Perceptron (MLP) with a contrastive metric learning loss function (MLP-CMLL)and Time Series K-means (TSKmeans) clustering. This dual approach, MLP-CMLL with TSKmeans, is crafted to refine cluster differentiation significantly. This method is designed to optimize cluster differentiation, bringing similar acoustic patterns closer and distancing divergent ones, thereby improving the MLP's ability to classify acoustic events over time. Addressing the limitations of traditional clustering algorithms in handling the temporal dynamics of AE data, MLP-CMLL with TSKmeans approach provides deeper insights into cluster formation and evolution. It promises enhanced monitoring and predictive maintenance capabilities in engineering applications by capturing the complex dynamics of AE data more effectively, offering a significant advancement in the field of structural health monitoring. Through experimental validation, we apply this method to characterize the loosening phenomenon in bolted structures under vibrations. Comparative analysis with two standard clustering methods using raw streaming data from three experimental campaigns demonstrates that our proposed method not only delivers valuable qualitative information concerning the timeline of clusters but also showcases superior performance in terms of cluster characterization.
How to Cite
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acoustic emission (AE), sequentially appearing clusters, data streams, structural health monitoring, contrastive metric learning, multi-layer perceptron (MLP)
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