In today’s world, where industrial production processes are rapidly digitalizing and being reshaped by intelligent systems, predictive maintenance systems have become not merely an option but an indispensable component of competitive manufacturing. This book addresses the transformation from traditional maintenance approaches to AI supported autonomous systems through a scientifically grounded and application-oriented perspective.

Throughout the book, a wide range of topics is covered from the fundamental principles of predictive maintenance to data analytics, machine learning algorithms, digital twins, explainable AI (XAI), hybrid models, sustainability, and sectoral applications. Each chapter has been structured to respond to the needs of both academic and industrial readers, supported by up-to-date literature.

The aim of this work is not only to provide in-depth knowledge on predictive maintenance but also to serve as a guiding resource for engineers, researchers, and decision-makers who wish to explore and develop the maintenance strategies of the future.

With the light of science, in pursuit of more reliable, efficient, and sustainable production systems…