Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications

ISBN-13: 9783662571736 / Angielski / Miękka / 2018 / 430 str.

Xiao-Sheng Si; Zheng-Xin Zhang; Chang-Hua Hu

Spis treści

Part I Introduction, Basic Concepts and Preliminaries.- Overview.- Advances in Data-Driven Remaining Useful Life Prognosis.- Model Determination for Lifetime Prognosis.- Part II Remaining Useful Life Prognosis for Linear Stochastic Degrading Systems.- Adaptive Remaining Useful Life Prediction Method.- Exact and Closed-Form Solution to Remaining Useful Life Prediction.- Remaining Useful Life Prediction With Three-Level Variability.- Part III Remaining Useful Life Prognosis for Nonlinear Stochastic Degrading Systems.- Nonlinear Degradation Modeling and Remaining Useful Life Prediction Method.- Hidden and Nonlinear Degradation Modeling and Online Remaining Useful Life Prediction Method.- Remaining Useful Life Prediction Method With State Switches.- Remaining Useful Life Prediction for Degrading Systems with Imperfect Maintenance.- Part IV Applications of Prognostics in Decision Making.- Variable Cost-based Maintenance Model from Prognostic Information.- Forecasting Spare Parts Demand under Prognostic Information.- Variable Cost-Based Maintenance and Inventory Model from Prognostic Information.- Prognostic-information-Based Order-Replacement Policy for Degrading Systems.

Blurb

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.