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Anomaly detection in Structural Health Monitoring using spectral distance and t-SNE–GMM framework under ambient excitation

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Abstract

Structural damage detection is crucial for ensuring the safety and reliability of civil infrastructure. In this paper, we propose a novel vibration-based structural damage detection framework using spectral distance and a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Gaussian Mixture Model (GMM). By integrating these three procedures, a unique, comprehensive, and highly effective approach for detecting structural damage is achieved. First, we extract spectral distance-based features from structural response data under ambient excitations, which provide information about the deviation of the spectral content of the signal from the undamaged structural response. Then we apply t-SNE to embed the dimensional data into lower dimensional feature space and visualize the data in a low-dimensional space. Finally, we use GMM-based clustering to classify the data into normal and anomalous classes. To evaluate the proposed approach, the experimental data from the Phase II Experimental Benchmark Problem on Structural Health Monitoring, introduced by the IASC-ASCE Structural Health Monitoring Task Group, is utilized. The results show that our method can effectively detect structural damage anomalies. Moreover, the proposed framework is computationally efficient, making it suitable for real-time Structural Health Monitoring (SHM) applications. This paper presents novel structural damage indicators (SDI) based on power spectral density (PSD) deviations between damaged and undamaged structural responses. The study compares the performance of proposed spectral distance measures and emphasizes the significance of spectral Kullback–Leibler distance as an indicator of structural deterioration. In addition, the effectiveness of t-SNE–GMM is demonstrated in efficiently segregating damage configurations.

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  1. Condition Monitoring Group, CSIR-Central Mechanical Engineering Research Institute, Durgapur, West Bengal, 713216, India

    S. K. Laha, B. Swarnakar & S. Kansabanik

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  1. S. K. Laha
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S. K. Laha developed the methodology and the relevant computer codes. All authors wrote the main manuscript. All authors reviewed the manuscript.

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Laha, S.K., Swarnakar, B. & Kansabanik, S. Anomaly detection in Structural Health Monitoring using spectral distance and t-SNE–GMM framework under ambient excitation. Asian J Civ Eng 25, 3711–3727 (2024). https://doi.org/10.1007/s42107-024-01006-y

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