Explainable Multimodal AI for Real-Time Industrial Fault Diagnosis in Edge Environments
Keywords:
explainable AI, multimodal learning, edge computing, industrial fault diagnosis, real-time systems, socio-technical infrastructure, system governanceAbstract
Industrial fault diagnosis has traditionally relied on single-modality sensor data and centralized processing pipelines that struggle to meet the latency and interpretability demands of modern manufacturing environments. The convergence of multimodal sensing, edge computing, and explainable artificial intelligence offers a transformative approach to real-time fault detection and root-cause analysis. This paper presents a comprehensive system-level examination of explainable multimodal AI architectures deployed on edge devices for industrial fault diagnosis. We analyze the structural trade-offs among model complexity, inference latency, explanation fidelity, and resource constraints inherent in edge environments. The discussion extends to governance frameworks for model updates, sustainability of continuous learning on resource-limited hardware, and fairness considerations when diagnostic decisions affect human operators and production workflows. Through cross-domain comparisons with autonomous driving and healthcare monitoring, we highlight transferable design principles. The paper further addresses policy implications regarding liability, auditability, and regulatory compliance in high-stakes industrial settings. We argue that the successful adoption of such systems depends not only on technical performance but also on the alignment of explanation methods with operator cognitive models and organizational decision processes. A forward-looking perspective outlines research frontiers in neuro-symbolic reasoning, federated learning for cross-factory knowledge sharing, and adaptive explanation generation that balances detail with actionable insight. This work aims to provide a foundational reference for researchers and practitioners developing trustworthy AI for industrial edge applications.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
