Paperback. Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0 unites data science, machine learning, IIOT, and AI to enable predictive and prescriptive maintenance across manufacturing, energy, transportation, agriculture, and healthcare. With contributions from leading academics and practitioners, the book bridges foundational principles with cutting-edge industrial case studies ranging from digital twins and anomaly detection to federated learning and secure healthcare analytics. Key Features: -Explains fundamental concepts of data analytics, AI, and machine learning for predictive maintenance. -Integrates IIoT, digital twins, federated learning, and blockchain into industrial maintenance strategies. -Demonstrates real-world applications across manufacturing, energy, healthcare, and agriculture sectors. -Analyzes optimization techniques, anomaly detection, condition monitoring, and RUL prediction models. -Addresses security and ethical issues, including hardware protection and homomorphic encryption for healthcare. -Maps future trends and emerging technologies driving predictive maintenance research. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Bibliographic Details

Title: Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0 (Paperback)
Publisher: Bentham Science Publishers
Publication Date: 2025
Language: English
ISBN 13: 9798898810894
Binding: Paperback
Condition: new

About this title

Synopsis

Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0 unites data science, machine learning, IIOT, and AI to enable predictive and prescriptive maintenance across manufacturing, energy, transportation, agriculture, and healthcare. With contributions from leading academics and practitioners, the book bridges foundational principles with cutting-edge industrial case studies ranging from digital twins and anomaly detection to federated learning and secure healthcare analytics.

Key Features:

-Explains fundamental concepts of data analytics, AI, and machine learning for predictive maintenance.
-Integrates IIoT, digital twins, federated learning, and blockchain into industrial maintenance strategies.
-Demonstrates real-world applications across manufacturing, energy, healthcare, and agriculture sectors.
-Analyzes optimization techniques, anomaly detection, condition monitoring, and RUL prediction models.
-Addresses security and ethical issues, including hardware protection and homomorphic encryption for healthcare.
-Maps future trends and emerging technologies driving predictive maintenance research.

"About this title" may belong to another edition of this title.

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