AI PREDICTIVE MAINTENANCE FOR ENGINEERS: Real-World Techniques for Fault Detection, Condition Monitoring, and Intelligent Asset Management.
About this audiobook
Predictive maintenance is transforming the way modern industries manage machines, equipment, and critical assets. AI PREDICTIVE MAINTENANCE FOR ENGINEERS is a practical guide for engineers, reliability professionals, maintenance managers, and technical leaders who want to apply artificial intelligence and machine learning to real-world maintenance challenges.
The book begins with the engineering foundations of predictive maintenance, explaining failure modes, physics of failure, reliability-centered maintenance, FMEA, maintenance maturity, and the business case for intelligent asset management. It then moves into the data foundation, covering vibration, temperature, thermal imaging, acoustic and ultrasonic monitoring, electrical signatures, oil analysis, process parameters, sensor selection, sampling strategies, industrial protocols, and edge-versus-cloud architectures.
Readers are guided through machine learning for maintenance applications, including supervised, unsupervised, and semi-supervised learning, feature engineering, classical algorithms, deep learning, anomaly detection, fault classification, explainable AI, model validation, and model drift. The book also explores continuous condition monitoring, digital twins, prognostics, uncertainty quantification, and remaining useful life estimation.
Beyond models and algorithms, the focus remains firmly on implementation. You will learn how to move from pilot projects to industrial deployment, integrate predictive insights with EAM and CMMS workflows, manage organizational adoption, measure financial value, and address cybersecurity and regulatory considerations.
With engineering-focused explanations, industrial case studies, practical exercises, Python notebooks, datasets, checklists, and a 100-day action plan, this book provides a field-oriented roadmap for building predictive maintenance systems that can move from data to actionable engineering decisions.
