Reliability-Centred Asset Management Under Uncertainty: Integrating Ai, Risk, and Human Factors: Methods, Models, and Real-World Applications for Comp

This book provides a unified and forward-looking framework for reliability-centred asset management under uncertainty, responding to the increasing complexity and risk exposure of modern engineering systems. It integrates reliability engineering, risk analysis, artificial intelligence, and human factors to support robust and transparent decision-making across the entire asset lifecycle.

The book moves beyond traditional reliability-based approaches by explicitly addressing both aleatory and epistemic uncertainty and their impact on engineering decisions. It presents state-of-the-art methods for uncertainty modelling, risk assessment, and multi-criteria decision-making, alongside data-driven and AI-based techniques for predictive maintenance, condition monitoring, and asset performance optimisation. Equal emphasis is placed on technical models and the role of human judgement, cognitive bias, and organisational influences on safety, reliability, and resilience.

Real-world case studies from energy, manufacturing, infrastructure, and maritime sectors demonstrate how the proposed methods can be implemented in practice, highlighting both benefits and challenges. The book also explores emerging developments such as digital twins, Industry 4.0 technologies, and resilience engineering, positioning asset management within the broader context of digital transformation.

Designed as both a reference and a learning resource, this book is intended for researchers, practicing engineers, asset managers, and advanced students seeking an integrated, practical, and scientifically rigorous approach to managing complex engineering assets under uncertainty.