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2017 | Book

Advances in Data-Driven RUL Prognosis Techniques Read chapter Read first chapter

Data-Driven Remaining Useful Life Prognosis Techniques

Stochastic Models, Methods and Applications

Authors: Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu

Publisher: Springer Berlin Heidelberg

Book Series : Springer Series in Reliability Engineering

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

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.

Table of Contents

Frontmatter

Introduction, Degradation Data Acquisition and Evaluation

Frontmatter
Chapter 1. Advances in Data-Driven RUL Prognosis Techniques
Abstract
Prognosis and health management (PHM) has drawn increasing attention and gained deepening recognition and widening applications during the past decades (Sandborn and Pecht, Microelectron Reliab 47(12):1847–1848, 2007, [1]; Dolev, IEEE Trans Reliab 58(2):262–263, 2009, [2]; Lau and Fong, Microelectron Reliab 51(2):253–254, 2011, [3]; Wang, IEEE Trans Reliab 60(1):2, 2011, [4]). Actually, the initial health and usage inspection system was first equipped in the early helicopters of US military, and the synthetically health management philosophy was presented for spacecraft in the 1970s.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 2. Planning Repeated Degradation Testing for Degrading Products
Abstract
Degradation information regarding the system’s health state, especially from highly reliable items, has been a useful alternative for the system’s remaining useful life (RUL) estimation, as well as a valuable basis for condition based maintenance (CBM). Once the degradation information of a system is available by the degradation test, one well-recognized method is to establish a stochastic degradation model to predict the distributions of the future degradation and the associated lifetime, based on the relationship between the degradation and failure time.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 3. Specifying Measurement Errors for Required Lifetime Estimation Performance
Abstract
Reliable and accurate lifetime estimates for key engineering assets have long been a hot research topic attracting increasing attention in reliability and operational research communities and practices.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu

Prognostic Techniques for Linear Degrading Systems

Frontmatter
Chapter 4. An Adaptive Remaining Useful Life Estimation Approach with a Recursive Filter
Abstract
Enhancing safety, efficiency, availability, and effectiveness of industrial and military systems through prognostics and health management (PHM) paradigm has gained momentum over the last decade (Pecht, Prognostics and health management of electronics, 2008, [1]; Si et al., Eur J Oper Res 213:1–14, 2011, [2]). PHM is a systematic approach that is used to evaluate the reliability of a system in its actual life cycle conditions, predict failure progression, and mitigate operating risks via management actions.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 5. An Exact and Closed-Form Solution to Degradation Path-Dependent RUL Estimation
Abstract
Prognostics and health management (PHM) is an efficient and systematic approach for evaluating the reliability of a system in its actual operating conditions, predicting failure progression, and mitigating operating risks via management actions (Pecht, Prognostics and health management of electronics, 2008, [1]). In PHM, prognostics can yield an advance warning of impending failure in a system, thereby helping in making maintenance decisions and executing preventive actions.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 6. Estimating RUL with Three-Source Variability in Degradation Modeling
Abstract
Prognostics and health management (PHM) can make full use of condition monitoring (CM) data from a functioning system to assess the reliability of the system in its actual life-cycle conditions
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu

Prognostic Techniques for Nonlinear Degrading Systems

Frontmatter
Chapter 7. RUL Estimation Based on a Nonlinear Diffusion Degradation Process
Abstract
Because of limited natural resources, considerably increased safety and environmental concerns, and the drive to reduce operating costs, critical assets need to be managed over their entire life cycles—from design, manufacture, sale, and operation to their end of life in order to optimize life cycle management and reduce negative impact on the environment [1, 2]. For safety-critical equipment, such as aviation control systems and nuclear power generators, the accurate and early estimation of failure is critical in order to avoid catastrophic events that may cause severe damage to equipment, loss of human lives, and environmental disasters.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 8. Prognostics for Age- and State-Dependent Nonlinear Degrading Systems
Abstract
Prognostics and health management (PHM) has been proved to be an effective methodology for improving reliability and reducing the operation risk of technological systems via management actions [1].
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 9. Adaptive Prognostic Approach via Nonlinear Degradation Modeling
Abstract
With the ever-increased high requirement of reliability and safety for critical systems, accurately assessing the pending failure of a system has become an active research area over the past decades.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 10. Prognostics for Hidden and Age-Dependent Nonlinear Degrading Systems
Abstract
With the rapid development of modern condition monitoring (CM) techniques, condition-based maintenance (CBM) which implements maintenance actions based on the CM information has become an active research area for reducing operation and maintenance costs.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 11. Prognostics for Nonlinear Degrading Systems with Three-Source Variability
Abstract
Thanks to the rapid development of information and sensing technologies, the degradation signals of a system can be obtained relatively easily using CM techniques, and the past decade has witnessed an increasingly growing research interest on the RUL estimate of systems based on the sensed degradation signals [1, 2].
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 12. RSL Prediction Approach for Systems with Operation State Switches
Abstract
Predicting the residual life is of significant importance in proactive maintenance, and prognostics and health management of systems (Pecht, Prognostics and health management of electronics, 2008, [1], Ye et al., Eur J Oper Res 221(2):360–367, 2012, [2], Lall et al., IEEE Trans Ind Electron 59(11):4301–4314, 2012, [3]). Many highly critical systems in military and aerospace fields, like missiles, rockets, and their associated systems, are required of long-term storage before used (Mclain and Warren, Automated reliability life data analysis of missiles in storage and flight, (1990), [4], Zhao et al., Qual Reliab Eng Int 11(1):123–127, 1995, [5], Luo et al., Research on storage life prediction method for strap-down inertial navigation system, 2012, [6]). For such systems, storage is an essential part of their lifecycles and the operating time of such systems is usually very short compared with the time in storage. Therefore, the investigation of the residual storage life (RSL) prediction is of significant importance in that it can help to plan efficient monitoring policy to extend the system’s life.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu

Applications of Prognostic Information

Frontmatter
Chapter 13. Reliability Estimation Approach for PMS
Abstract
Many complex systems are designed to perform missions that consist of several phases in which the deterioration and configuration of systems may change from phase to phase. These systems are called phased-mission systems (PMSs) [1]. PMSs are formally defined to be the systems subject to multiple, consecutive, nonoverlapping phases of operation required to finish the final product or service [2]. A typical PMS is the on-board systems for the aided guide of aircraft, whose mission consists of takeoff, ascent, cruise, approach, and landing phases. For mission success, all phases must be completed without failure. Other PMSs include safety-critical systems (such as aerospace systems and weapon systems), and modern manufacturing processes (e.g., assembly, machining, semiconductor fabrication, and pharmaceutical manufacturing) [3]. As an important measure for system design, operation, and maintenance of PMSs [4, 5], reliability can be used to quantify the performance of PMSs. Accurate estimation of the reliability is very helpful for efficient maintenances and logistic supports of such systems, which actually lead to lifecycle cost reduction and the avoidance of catastrophic failures.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 14. A Real-Time Variable Cost-Based Maintenance Model
Abstract
With advances in condition monitoring technologies, the past decade has witnessed an increasingly growing research interest on various aspects of degradation modeling for prognostics from the observed signals by dedicated sensors [1‐4].
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 15. An Adaptive Spare Parts Demand Forecasting Method Based on Degradation Modeling
Abstract
System prognostics and health management (PHM) is a new health management methodology proposed for complex engineering systems to reduce maintenance costs, improve the system operating reliability and safety, and mitigate the failure risk [1, 2]
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Chapter 16. Variable Cost-Based Maintenance and Inventory Model
Abstract
The traditional maintenance and spare parts inventory decision models mainly rely on using population-specific reliability distribution, which cannot reflect the different degradation characteristics of single in-service equipment.
Xiao-Sheng Si, Zheng-Xin Zhang, Chang-Hua Hu
Metadata
Title
Data-Driven Remaining Useful Life Prognosis Techniques
Authors
Xiao-Sheng Si
Zheng-Xin Zhang
Chang-Hua Hu
Copyright Year
2017
Publisher
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-54030-5
Print ISBN
978-3-662-54028-2
DOI
https://doi.org/10.1007/978-3-662-54030-5