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

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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications
Author:
Hu ChangHua / Si XiaoSheng / Zhang ZhengXin
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Springer Berlin Heidelberg
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2017
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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.;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.

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Part I
Introduction, Degradation Data Acquisition and Evaluation

National Defense Industry Press and Springer-Verlag GmbH Germany 2017

Xiao-Sheng Si , Zheng-Xin Zhang and Chang-Hua Hu Data-Driven Remaining Useful Life Prognosis Techniques Springer Series in Reliability Engineering 10.1007/978-3-662-54030-5_1

1. Advances in Data-Driven RUL Prognosis Techniques

Xiao-Sheng Si 1 , Zheng-Xin Zhang 1 and Chang-Hua Hu 1

(1)

Department of Automation, Xian Institute of High-Technology, Xian, Shaanxi, China

Xiao-Sheng Si

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1.1 Introduction

Prognosis and health management (PHM) has drawn increasing attention and gained deepening recognition and widening applications during the past decades [], as well as commercial companies such as Boeing have launched a great deal of theoretical and applied research works about PHM technology. The PHM conference has been successfully organized and held by IEEE Reliability Society in Shenzhen, Macau, Beijing, Rome, and Zhangjiajie respectively, Beijing in six consecutive years since 2010.

Remaining useful life (RUL) estimation, offering guidance for sequential management involving inspection schedule, maintenance, replacement and spare parts ordering, has been considered as the kernel technology of PHM, and the focus of current research in the field of reliability also. According to Petchs classical monograph about PHM technology []. While being satisfactory for RUL estimation under each specific applying condition, these methods exhibit some limits in cases with heterogeneity from the inner states or the external operating conditions of systems.

Heterogeneity is widespread in the inner states of the system and the related working environments. Examples involve that a weapon system may experience various operating conditions, saying storage, inspection, transport, and maintenance during its life cycle due to different tasks; that a manufacture system produces different products under different workloads; and that even systems from the same category may exhibit various degrading paths in the same environment. The performance degradation of a system is a result of interactions of both inner deterioration and working environment of the system, indicating a need for incorporating the heterogeneity into degradation modeling, to achieve a more accurate RUL estimation. For particular heterogeneity, such as the unit-to-unit variability, changing working conditions and periodic tasks, many recent advances in RUL estimation have appeared. However, to the best of the authors knowledge, there is still no review regarding degradation modeling and RUL estimation for systems with heterogeneity. Therefore, this chapter tries the best to fill this gap.

Toward the end of this chapter, three kinds of heterogeneity are considered consecutively: the unit-to-unit variability for systems from the same category, the variability in time-varying operating conditions, and the diversity of tasks and workloads of system during their life cycles. The first kind of variability describes the differences in degradation processes of units from the same category, while the second represents noninform working conditions related to the degradation, such as the time-varying, multi-state and stochastic working environments or random shocks. The third kind of heterogeneity captures the influence of changes in tasks and management activities involving inspection, maintenance, etc. Accordingly, this chapter classifies methods addressing degradation modeling and RUL estimation with heterogeneity into three kinds, each of which considers one kind of heterogeneity introduced above and consists of some subclassifications. The taxonomy of RUL estimation approaches for systems with heterogeneity is illustrated in Fig..

Fig. 1.1

Taxonomy of RUL estimation approaches for system under heterogeneity

The remainder of the chapter is structured as follows: Section concludes the chapter and provides several possible directions for future studies.

1.2 Methods Considering Unit-to-Unit Variability

A large number of experiments and engineering phenomena show that systems of the same category, even from one batch degrade differently from one another in performance. This kind of difference in degradation is usually defined as the unit-to-unit variability, due to the variability in inner structures of the considered systems, as well as the diversity in their working environment. Commonly, models with random effects are employed to capture the unit-to-unit variability, when we model the degradation process and estimate the RUL. The most typical way to do so is to specify some parameters of the model as random variables governed by distributions with computing convenience, presenting the individuality in degradation processes from different units and leave the rest of parameters as constants describing the universality in degradation of systems from the same category or batch. In the following, random coefficients regression models and stochastic process models with random coefficients of this kind are discussed, respectively.

1.2.1 Random Coefficients Regression Models

Random effects first appeared in random coefficients regression models. In the most frequently cited paper about degradation modeling and RUL estimate [], Lu and Meeker described the random coefficients regression model in a general form as

Data-Driven Remaining Useful Life Prognosis Techniques Stochastic Models Methods and Applications - image 2

where

is the amount of the degradation of the i th device in the j th inspecting time

, the fixed coefficients

and the random coefficients

are, respectively, used to characterize the universality and individuality in degradation of different systems, and is the random noise.

Random coefficients regression models have been extended, developed and applied widely in many areas, in which a series of extended works presented by Gebraeel et al. are included []. A procedure deciding the minimum sample size and the minimum times of systematic sampling for each item to achieve an anticipant accuracy of estimation (large sample approximate variance) has been provided in their works.

However, according to Wangs analysis in [], the assumptions of random coefficient regression models result in several limitations, involving the need for more historical degradation data from different systems of the same category, the difficulty in capturing the time-varying dynamics of systems and the independency between random noise with time.

1.2.2 Stochastic Process Models with Random Coefficients

Incorporating random coefficients into stochastic degradation process-based models enables both considerations of time-varying dynamics of an individual system, and description of unit-to-unit variability, and thus has been favored by many researchers. Suppose that the degradation of a system is modeled by a stochastic process Data-Driven Remaining Useful Life Prognosis Techniques Stochastic Models Methods and Applications - image 7

Data-Driven Remaining Useful Life Prognosis Techniques Stochastic Models Methods and Applications - image 7

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