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.

翻译：基于振动响应（如传递函数）的聚类在结构异常检测中具有广阔前景，但现有方法大多难以确定最优聚类数量并处理高维流数据，同时其浅层结构也使其对人工设计特征的质量较为敏感。为弥补这一不足，本研究提出Dirichlet过程-深度生成模型集成的增量学习（DPGIIL）聚类方法，该方法结合了深度生成模型在表征学习方面的优势与Dirichlet过程混合模型在识别观测数据中不同模式的能力。通过在深度生成模型的隐空间引入Dirichlet过程混合模型先验，DPGIIL能够自动捕捉提取的隐表征间的差异性，从而实现生成建模与聚类的双重功能。在变分贝叶斯推断框架下，本文解析推导出DPGIIL对数边缘似然的下界（该下界在充足训练数据条件下比证据下界更紧），实现了深度生成模型与Dirichlet过程混合模型参数的联合优化，使得Dirichlet过程混合模型能够对深度生成模型的特征提取过程进行正则化。此外，设计了基于贪心分裂合并策略的坐标上升变分推断方法以加速优化过程。Dirichlet过程混合模型的汇总统计量与网络参数共同用于保留历史数据信息以实现增量学习。值得注意的是，本研究以变分自编码器作为DPGIIL框架内的示例模型，但该框架可适配其他深度生成模型。两个案例研究表明，所提方法在结构异常检测与聚类任务中优于若干先进方法，并能动态生成新聚类以指示在线监测中新结构状态的出现。