AI-Empowered Machinery Fault Diagnostics and Prognostics in Smart Manufacturing

AI-Empowered Machinery Fault Diagnostics and Prognostics in Smart Manufacturing

By Deepam Goyal, Shankar Sehgal, Kuldeep Kumar Saxena, and Zheng Liu

Recent advancements in smart manufacturing have fundamentally transformed industrial operations—from machine-centric processes to ecosystems driven by data-centric, intelligent decision-making. At the heart of this transformation lies artificial intelligence (AI), which has emerged as a powerful enabler for intelligent fault diagnostics, prognostics, and predictive maintenance. By empowering machines to anticipate health conditions with unprecedented accuracy and precision, AI is redefining operational excellence, safety, sustainability, and resilience in Industry 4.0 and beyond.

AI-Empowered Machinery Fault Diagnostics and Prognostics in Smart Manufacturing provides a comprehensive and systematic exploration of AI-driven methodologies for machinery health management. The book bridges the gap between traditional maintenance strategies and modern AI-enabled solutions, offering both theoretical foundations and practical insights. It is designed for researchers, scientists, engineers, and industry practitioners seeking to leverage AI for intelligent monitoring, diagnostics, and prognostics of industrial machinery.

Part I: Foundations and Methodological Approaches establishes the theoretical underpinnings of AI-based fault diagnostics and prognostics. It presents a broad overview of state-of-the-art AI techniques used in both academic research and industrial practice, with a strong emphasis on model interpretability—an essential requirement for real-world industrial adoption.

Part II: Core Diagnostic Techniques and Methods introduces classical diagnostic approaches before advancing to specialized vibration-based techniques for diverse machinery systems. This section ensures readers develop a solid conceptual understanding of conventional methods, creating a critical foundation for appreciating AI-enhanced manufacturing solutions.

Part III: Advanced AI and Machine Learning Applications forms the core of the book, exploring cutting-edge approaches such as deep learning architectures, transfer learning, and graph neural networks. These transformative techniques enable accurate, adaptive, and scalable diagnostics and prognostics, addressing the challenges posed by complex, data-rich industrial systems.

Part IV: Industry 4.0/5.0 Integration and Specialized Applications focuses on translating AI technologies into real-world manufacturing environments. It highlights human–AI collaboration, demonstrating how intelligent systems can be integrated into smart factories to enhance productivity, reliability, and decision-making efficiency.

By combining fundamental principles with the latest industrial innovations, this book equips readers with the foresight and technical expertise needed to design and deploy AI-empowered solutions in modern smart manufacturing. It envisions a future where machinery can learn, adapt, and anticipate its own condition—enabling intelligent decision-making and corrective actions that drive energy efficiency, reliability, and sustainable industrial progress.