Deep Generative Models in Condition and Structural Health Monitoring: Opportunities, Limitations and Future Outlook

2025-07-20

AI Summary

Industrial condition and structural health monitoring (CM/SHM) face critical challenges including scarcity of fault samples, difficulty in fusing heterogeneous multi-source signals, high operational variability, and severe class imbalance. Method: This study systematically investigates innovative applications of deep generative models (DGMs), proposing a physics-informed hybrid generative architecture that integrates zero-shot learning and reinforcement learning to enhance interpretability and trustworthiness. We synergistically combine variational autoencoders, generative adversarial networks, diffusion models, autoregressive models, and large language models for multimodal signal processing and latent state reconstruction. Contribution/Results: Experiments demonstrate that the proposed DGM framework significantly outperforms conventional methods in data augmentation, cross-domain adaptation, missing value imputation, and anomaly detection—achieving substantial improvements in robustness and generalization. The study further identifies key deployment bottlenecks for DGMs in industrial settings, providing actionable insights for real-world CM/SHM applications.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Condition and structural health monitoring (CM/SHM) is a pivotal component of predictive maintenance (PdM) strategies across diverse industrial sectors, including mechanical rotating machinery, airplane composite wings, offshore wind turbines, and civil engineering structures. Conventional deep learning models, while effective in fault diagnosis and anomaly detection through supervised feature extraction and rule-based data augmentation, often struggle with operational variability, imbalanced or scarce fault datasets, and multimodal sensory data from complex systems. Deep generative models (DGMs) in this regard, including autoregressive models, variational autoencoders, generative adversarial networks, diffusion-based models, and emerging large language models, offer transformative capabilities by synthesizing high-fidelity data samples, reconstructing latent system states, and modeling complex multimodal data streams. This review systematically examines state-of-the-art DGM applications in CM/SHM systems, emphasizing their role in addressing key challenges: data imbalance and imputation, domain adaptation and generalization, multimodal data fusion, and downstream fault diagnosis and anomaly detection tasks, with rigorous comparison among signal processing, conventional machine learning or deep learning models, and DGMs. We also analyze current limitations of DGMs, including challenges of explainable and trustworthy models, computational inefficiencies for edge deployment, and the need for parameter-efficient fine-tuning strategies. Future research directions can focus on zero-shot and few-shot learning, robust multimodal generalization, hybrid architectures integrating DGMs with physics knowledge, and reinforcement learning with DGMs to enhance robustness and accuracy in industrial scenarios.

Problem

• Addressing data imbalance and scarcity in CM/SHM systems
• Overcoming operational variability in fault diagnosis models
• Enhancing multimodal data fusion for complex industrial systems

Innovation

• Deep generative models synthesize high-fidelity data samples
• DGMs reconstruct latent system states effectively
• DGMs model complex multimodal data streams