DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection

• URL: http://arxiv.org/abs/2412.04781v1
• Date: Fri, 06 Dec 2024 05:18:58 GMT
• Title: DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection
• Authors: Lin-Feng Mei, Wang-Ji Yan,
• Abstract summary: This work proposes the Dirichlet process-deep generative model-integrated incremental learning (DPGIIL) for clustering. By introducing a DPMM prior to the latent space of DGMs, DPGIIL automatically captures dissimilarities in extracted latent representations, enabling both generative modeling and clustering. Two case studies show that the proposed method outperforms some state-of-the-art approaches in structural anomaly detection and clustering.
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• Abstract: Clustering based on vibration responses, such as transmissibility functions (TFs), is promising in structural anomaly detection, but most existing approaches struggle with determining the optimal cluster number and handling high-dimensional streaming data, while their shallow structures also make them sensitive to manually-engineered feature quality. To bridge this gap, this work proposes the Dirichlet process-deep generative model-integrated incremental learning (DPGIIL) for clustering by combining the advantages of deep generative models (DGMs) in representation learning and the Dirichlet process mixture model (DPMM) in identifying distinct patterns in observed data. By introducing a DPMM prior into the latent space of DGMs, DPGIIL automatically captures dissimilarities in extracted latent representations, enabling both generative modeling and clustering. Within the context of variational Bayesian inference, a lower bound on the log marginal likelihood of DPGIIL, tighter than the evidence lower bound given sufficient training data, is derived analytically, which enables the joint optimization of DGM and DPMM parameters, thereby allowing the DPMM to regularize the DGM's feature extraction process. Additionally, a greedy split-merge scheme-based coordinate ascent variational inference method is devised to accelerate the optimization. The summary statistics of the DPMM, along with the network parameters, are used to retain information about previous data for incremental learning. Notably, this study uses variational autoencoder (VAE) within DPGIIL as an illustrative example, while this framework is adaptable to other DGMs. Two case studies show that the proposed method outperforms some state-of-the-art approaches in structural anomaly detection and clustering, while also dynamically generating new clusters to indicate the emergence of new structural conditions for online monitoring.

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