Introduction

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.

Readership:

Ideal for researchers, postgraduate students, and industry professionals in data science, AI, mechanical engineering, industrial automation, and smart manufacturing. Also valuable for policymakers, consultants, and technology developers designing predictive maintenance systems for Industry 4.0.