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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Dr. K. Tanuja Alekhya
Dr. K. Tanuja Alekhya is a researcher and practitioner specializing in AI-driven predictive maintenance, Industry 4.0, and Model-as-a-Service architectures. With deep expertise in hybrid deep learning, IoT data integration, and cloud-native deployment frameworks, she has contributed to both academia and industry by bridging the gap between advanced AI research and real-world implementation.
Her doctoral research focused on developing a hybrid predictive maintenance framework capable of handling multi-modal industrial data, which evolved into the MaaS blueprint presented in this book. She has collaborated with global enterprises on AI-enabled business models, MLOps strategies, and scalable deployment architectures.
Dr. K. Tanuja Alekhya is passionate about applied AI, digital transformation, and sustainable industrial intelligence, and her work reflects a commitment to creating solutions that are academically rigorous, practically deployable, and strategically transformative.
