Data-Driven Remaining Useful Life Prognosis Techniques

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail.

The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.

Describes the basic data-driven remaining useful life prognosis theory systematically and in detail Includes a wealth of degradation monitoring experiment data, practical prognosis methods, and various decision-making applications that employ prognostic information Highlights new findings on remaining useful life prognosis techniques for linear/nonlinear systems Provides a complete picture of prognostic information-based decision-making applications Includes supplementary material: sn.pub/extras

Inhalt
From the Contents: Part I Introduction, Basic Concepts and Preliminaries.- Overview.- Advances in Data-Driven Remaining Useful Life Prognosis.- Part II Remaining Useful Life Prognosis for Linear Stochastic Degrading Systems.- Part III Remaining Useful Life Prognosis for Nonlinear Stochastic Degrading Systems.- Part IV Applications of Prognostics in Decision Making.- Variable Cost-based Maintenance Model from Prognostic Information.