Modeling Remaining Useful Life Dynamics in Reliability Engineering applies traditional reliability engineering methods to Prognostics and Health Management (PHM), looking at Remaining Useful Life (RUL) and predictive maintenance to enable engineers to effectively and safely predict machinery lifespan. One of the key tools used in defining and implementing predictive maintenance policies is the RUL indicator. However, it is essential to account for the uncertainty inherent to the RUL, as otherwise predictive maintenance strategies can be incorrect. This can cause high costs, or alternatively, ineffective predictions. Methods used to estimate RUL are very numerous and diverse, and broadly speaking, fall into three categories: model-based, data-driven, or hybrid, which uses both. The book starts by building on established theory, and applying cutting edge research to it, such as artificial intelligence models and deep learning. It looks at traditional reliability engineering methods through their relation to Prognostics and Health Management (PHM) requirements and presents the concept of RUL loss rate. Following on from this, the book presents a general method for defining a nonlinear transformation enabling the MRL to become a linear function. It also touches on topics such as Weibull distribution, gamma distribution and degradation, along with time-to-failure distributions. Features: Provides both practical and theoretical background of RUL Describes how the uncertainty of RUL can be related to RUL loss rate Provides new insights into time-to-failure distributions Offers tools for predictive maintenance_ The book will be of interest to engineers and researchers in reliability engineering, Prognostics and Health Management and industry management.