Anomaly and Fault Detection and Predictive Maintenance: a Review on Industrial Applications

Frizzo, Davide and Zaccaria, Valentina and Petrovic, David and Borsatti, Francesco and Sartor, Davide and Russo, Felice and Marcario, Genoveffa and Susto, Gian Antonio (2026) Anomaly and Fault Detection and Predictive Maintenance: a Review on Industrial Applications. INFOCOMMUNICATIONS JOURNAL, 18 (SI). pp. 44-103. ISSN 2061-2079

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Abstract

Diagnostics and prognostics are crucial components of Smart Factories. Their continuous development is driven by the Industry 4.0 and 5.0 revolution, which relies on Artificial Intelligence (AI), Internet of Things (IoT) and Industrial Internet of Things (IIoT). This survey provides a comprehensive review of AI-enhanced Anomaly Detection (AD), Fault Detection (FD), and Predictive Maintenance (PdM) frameworks, considering both state-of-the-art methodologies and practical applications across diverse industrial domains. Key aspects covered include benchmark datasets, the integration of IoT/IIoT infrastructures, and the economic implications of diagnostics and prognostics in smart manufacturing. Moreover, the emphasis on the humancentric vision of Industry 5.0, the significance of Explainable Artificial Intelligence (XAI) and Uncertainty Quantification (UQ) for fostering transparent and trustworthy industrial AI systems is highlighted. Finally, research gaps and future research directions are considered, notably in model transferability, computational efficiency, and human-centric AI development for realworld deployment.

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