Overview

Predictive maintenance is no longer just a technical tool; it is the backbone of Industry 4.0 transformation. This book, Plug-and-Play Predictive Maintenance in Industry 4.0, introduces a Model-as-a-Service (MaaS) framework that bridges the gap between academic research and industrial deployment. Built on a foundation of hybrid deep learning architectures, the framework integrates multiple data modalities-sensor time-series, tabular records, and image-based signals-to deliver accurate, explainable, and actionable insights. Unlike conventional research prototypes, this approach is designed for plug-and-play deployment, ensuring scalability across manufacturing, energy, logistics, healthcare, and smart infrastructure. With features such as model zoo orchestration, automated best-model selection, post-processing layers, explainability modules, and auto-retraining, the system provides a holistic blueprint for predictive and prescriptive maintenance. This book blends rigorous academic research with practical design principles, making it invaluable for researchers, engineers, industry leaders, and policymakers seeking to leverage AI for operational excellence.

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