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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley Series in Quality and Reliability Engineering
- Sprache: Englisch
Offers a holistic approach to guiding product design, manufacturing, and after-sales support as the manufacturing industry transitions from a product-oriented model to service-oriented paradigm
This book provides fundamental knowledge and best industry practices in reliability modelling, maintenance optimization, and service parts logistics planning. It aims to develop an integrated product-service system (IPSS) synthesizing design for reliability, performance-based maintenance, and spare parts inventory. It also presents a lifecycle reliability-inventory optimization framework where reliability, redundancy, maintenance, and service parts are jointly coordinated. Additionally, the book aims to report the latest advances in reliability growth planning, maintenance contracting and spares inventory logistics under non-stationary demand condition.
Reliability Engineering and Service provides in-depth chapter coverage of topics such as: Reliability Concepts and Models; Mean and Variance of Reliability Estimates; Design for Reliability; Reliability Growth Planning; Accelerated Life Testing and Its Economics; Renewal Theory and Superimposed Renewals; Maintenance and Performance-Based Logistics; Warranty Service Models; Basic Spare Parts Inventory Models; Repairable Inventory Systems; Integrated Product-Service Systems (IPPS), and Resilience Modeling and Planning
- Guides engineers to design reliable products at a low cost
- Assists service engineers in providing superior after-sales support
- Enables managers to respond to the changing market and customer needs
- Uses end-of-chapter case studies to illustrate industry best practice
- Lifecycle approach to reliability, maintenance and spares provisioning
Reliability Engineering and Service is an important book for graduate engineering students, researchers, and industry-based reliability practitioners and consultants.
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Veröffentlichungsjahr: 2018
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Wiley Series in Quality & Reliability Engineering
Dr Andre Kleyner
Series Editor
The Wiley series in Quality & Reliability Engineering aims to provide a solid educational foundation for both practitioners and researchers in Q&R field and to expand the reader's knowledge base to include the latest developments in this field. The series will provide a lasting and positive contribution to the teaching and practice of engineering.
The series coverage will contain, but is not exclusive to,
statistical methods;
physics of failure;
reliability modeling;
functional safety;
six‐sigma methods;
lead‐free electronics;
warranty analysis/management; and
risk and safety analysis
Wiley Series in Quality & Reliability Engineering
Reliability Engineering and Services
by Tongdan Jin
November 2018
Design for Safety
by Louis J Gullo and Jack Dixon
February 2018
Thermodynamic Degradation Science: Physics of Failure, Accelerated Testing, Fatigue, and Reliability Applications
by Alec Feinberg
October 2016
Next Generation HALT and HASS: Robust Design of Electronics and Systems
by Kirk A. Gray and John J. Paschkewitz
May 2016
Reliability and Risk Models: Setting Reliability Requirements
, 2nd Edition
by Michael Todinov
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Applied Reliability Engineering and Risk Analysis: Probabilistic Models and Statistical Inference
by Ilia B. Frenkel, Alex Karagrigoriou, Anatoly Lisnianski, and Andre V. Kleyner
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Design for Reliability
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July 2012
Effective FMEAs: Achieving Safe, Reliable, and Economical Products and Processes Using Failure Modes and Effects Analysis
by Carl Carlson
April 2012
Failure Analysis: A Practical Guide for Manufacturers of Electronic Components and Systems
by Marius Bazu and Titu Bajenescu
April 2011
Reliability Technology: Principles and Practice of Failure Prevention in Electronic Systems
by Norman Pascoe
April 2011
Improving Product Reliability: Strategics and Implementation
by Mark A. Levin, Ted T. Kalal
March 2003
Test Engineering: A Concise Guide to Cost‐Effective Design, Development, and Manufacture
by Patrick O'Connor
April 2001
Integrated Circuit Failure Analysis: A Guide to Preparation Techniques
by Friedrich Beck
January 1998
Measurement and Calibration Requirements for Quality Assurance to ISO 9000
by Alan S. Morris
October 1997
Electronic Component Reliability: Fundamentals, Modeling, Evaluation, and Assurance
by Finn Jensen
November 1995
Reliability Engineering and Services
Tongdan Jin
Ingram School of Engineering Texas State University, USA
Copyright
This edition first published 2019
© 2019 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Tongdan Jin to be identified as the author of this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication Data
Names: Jin, Tongdan, author.
Title: Reliability engineering and services / Dr. Tongdan Jin, Professor,
Texas State University, USA.
Description: Hoboken, NJ, USA : John Wiley & Sons, Inc., [2019] | Includes
bibliographical references and index. |
Identifiers: LCCN 2018031844 (print) | LCCN 2018032734 (ebook) | ISBN
9781119167037 (Adobe PDF) | ISBN 9781119167044 (ePub) | ISBN 9781119167013
(hardcover)
Subjects: LCSH: Reliability (Engineering)
Classification: LCC TS173 (ebook) | LCC TS173 .J56 2018 (print) | DDC
620/.00452–dc23
LC record available at https://lccn.loc.gov/2018031844
Cover Design: Wiley
Cover Images: Modern commuter train © Sailorr/Shutterstock;
Wind farm © William C. Y. Chu/Getty Images;
Airplane © 06photo/Shutterstock; Drone © Mopic/Fotolia
Dedication
To Youping and Ankai
Series Editor's Foreword
The Wiley Series in Quality & Reliability Engineering aims to provide a solid educational foundation for researchers and practitioners in the field of quality and reliability engineering and to expand the knowledge base by including the latest developments in these disciplines.
The importance of quality and reliability to a system can hardly be disputed. Product failures in the field inevitably lead to losses in the form of repair cost, warranty claims, customer dissatisfaction, product recalls, loss of sale, and, in extreme cases, loss of life.
With each year engineering systems are becoming more and more complex, with added functions and capabilities; however, the reliability requirements remain the same or grow even more stringent due to the proliferation of functional safety standards and rising expectations of quality and reliability on the part of the product end user. The rapid development of automotive electronic systems, eventually leading to autonomous driving, also puts additional pressure on the reliability expectations for these systems.
However, despite its obvious importance, quality and reliability education is paradoxically lacking in today's engineering curriculum. Very few engineering schools offer degree programs or even a sufficient variety of courses in quality or reliability methods. The topics of accelerated testing, reliability data analysis, renewal systems, maintenance, HALT/HASS, warranty analysis and management, reliability growth and other practical applications of reliability engineering receive little coverage in today's engineering student curriculum. Therefore, the majority of quality and reliability practitioners receive their professional training from colleagues, professional seminars, and professional publications. The book you are about to read is intended to close this educational gap and provide additional learning opportunities for a wide range of readers from graduate level students to seasoned reliability professionals.
We are confident that this book, as well as this entire book series, will continue Wiley's tradition of excellence in technical publishing and provide a lasting and positive contribution to the teaching and practice of reliability and quality engineering.
Dr. Andre Kleyner
Editor of the Wiley Series in Quality & Reliability Engineering
Preface
Reliability engineering is a multidisciplinary study that deals with the lifecycle management of a product or system, ranging from design, manufacturing, and installation to maintenance and repair services. Reliability plays a key role in ensuring human safety, cost‐effectiveness, and resilient operation of infrastructures and systems. It has been widely accepted as a critical performance measure in both private and public sectors, including manufacturing, healthcare, transportation, energy, chemical and oil refinery, aviation, aerospace, and defense industries. For instance, commercial airplane engines can fly over 5000 hours before the need for overhaul and maintenance. This means that the plane can cross the Pacific Ocean nearly 500 times without failure. In road transportation, China has constructed a total of 16 000 km of high‐speed rail since 2008 and the annual ridership is three billion. The service reliability reaches 0.999 999 998 given the annual fatality of five passengers on average. The F‐35 is the next generation of jet fighters for the US Air Force. It is anticipated that 2000 aircraft will be deployed in the next 50 years. The design and manufacturing of these aircraft will cost $350 billion, yet the maintenance and support of the fleet is expected to be $600 billion. These examples indicate the success in deploying and operating a new product is highly dependent upon the reliability, maintenance, and repair services during its use.
This book aims to offer a holistic reliability approach to product design, testing, maintenance, spares provisioning, and resilience operations. Particularly, we present an integrated product‐service system with which the design for reliability, performance‐based maintenance, and spare parts logistics are synthesized to maximize the reliability while lowering the cost. Such a lifecycle approach is imperative as the industry is transitioning from a product‐oriented model to a service‐centric paradigm. We report the fundamental knowledge and best industry practices in reliability modeling, maintenance planning, spare parts logistics, and resilience planning across a variety of engineering domains. To that end, the book is classified into four topics: (1) design for reliability; (2) maintenance and warranty planning; (3) product and service integration; and (4) engineering resilience modeling. Each topic is further illustrated below.
Chapters 1 to 5 are dedicated to the design for reliability. They cover a wide array of reliability modeling and design methods, including non‐parametric models, parametric models, reliability block diagrams, min‐cut, and min‐path network theory, importance measures, multistate systems, reliability and redundancy allocation, multicriteria optimization, fault‐tree analysis, failure mode effects and criticality analysis, latent failures, corrective action and effectiveness, multiphase reliability growth planning, power law model, and accelerated life testing.
Chapters 6 to 8 focus on maintenance and warranty planning that deals with the decision making on replacement and repair of field units. Technical subjects include renewal theory, superimposed renewal, corrective maintenance, preventive maintenance, condition‐based maintenance, performance‐based maintenance, health diagnostics and prognostics management, repairable system theory, no‐fault‐found issue, free‐replacement warranty, pro‐rata warranty, extended warranty services, and two‐dimensional warranty policy.
Chapters 9 to 11 model and design integrated product‐service offerring systems. First, basic inventory models are reviewed, including economic order quantity, continuous and period review policy with deterministic and stochastic lead time, respectively. Then the analyses are directed to repairable inventory systems that face stationary (or Poisson) demand or non‐stationary demand processes. Multiresolution and adaptive inventory replenishment policy are applied to cope with the time‐varying demand rate. Both single‐echelon and multi‐echelon inventory models are analyzed. Finally, an integrated production‐service system that jointly optimizes reliability, maintenance, spares inventory, and repair capacity are elaborated in the context of multiobjective, performance‐based contracting.
Chapter 12 introduces the basic concepts and modeling methods in resilience engineering. Unlike reliability issues, events considered in resilience management possess two unique features: high impact with low occurrence probability and catastrophic events with cascading failure. We present several resilience performance measures derived from the resilience curve and further discuss the difference between reliability and resilience. The chapter concludes by emphasizing that prevention, survivability, and recoverability are the three main aspects in resilience management.
This book represents a collection of the recent advancements in reliability theory and applications, and is a suitable reference for senior and graduate students, researcher scientists, reliability practitioners, and corporate managers. The case studies at the end of the chapters assist readers in finding reliability solutions that bridge the theory and applications. In addition, the book also benefits the readers in the following aspects: (1) guide engineers to design reliable products at a low cost; (2) assist the manufacturing industry in transitioning from a product‐oriented culture to a service‐centric organization; (3) support the implementation of a data‐driven reliability management system through real‐time or Internet‐based failure reporting, analysis, and corrective actions system; (4) achieve zero downtime equipment operation through condition‐based maintenance and adaptive spare parts inventory policy; and (5) realize low‐carbon and sustainable equipment operations by repairing and reusing failed parts.
In summary, reliability engineering is evolving rapidly as automation and artificial intelligence are becoming the backbone of Industry 4.0. New products and services will constantly be developed and adopted in the next 10 to 20 years, including autonomous driving, home robotics, delivery drones, unmanned aerial vehicles, electric cars, augmented virtual reality, smart grids, Internet of Things, cloud and mobile computing, and supersonic transportation, just to name a few. The introduction and deployment of these new technologies require the innovation in reliability design, modeling tools, maintenance strategy, and repair services in order to meet the changing requirements. Therefore, emerging technologies, such as big data analytics, machine learning, neural networks, renewable energy, additive and smart manufacturing, intelligent supply chain, and sustainable operations will lead the initiatives in new product introduction, manufacturing, and after-sales support.
Tongdan Jin
San Marcos, TX 78666, USA
Acknowledgement
This book received a wide range of support and assistance during its development stage. First, I would like to thank Ms Ella Mitchell, assistant editor in Electrical Engineering at Wiley. Without her early outreach and encouragement, I would not have been able to lay out the preliminary proposal and start this writing journey.
I also want to thank the early assistance from Ms Shivana Raj, Ms Deepika Miriam, and Ms Sharon Jeba Paul, who served as the editorial contacts during the formation of the first four chapters of the book. My great appreciation is given to Mr Louis Vasanth Manoharan who provided the assistance, communications, and editorial guidelines when the remaining eight chapters were finally completed.
I am also indebted to Ms Michelle Dunckley and the design team for their creation of the nice book cover. Special appreciation is given to Ms Patricia Bateson for her professional and quality editing of the entire manuscript. My thanks are also to production editor Mr. Sathishwaran Pathbanabhan for the final quality check of the editing.
Meanwhile, I would like to thank Dr Shubin Si and Dr Hongyan Dui for the discussion and formation of integrated importance measures in Chapter 2. My appreciation is extended to Dr Zhiqiang Cai who invited me to offer reliability engineering workshops at Northwestern Polytechnical University, Xian, where the materials were used by both Masters and PhD students. Very sincerely, I want to thank the Ingram School of Engineering at Texas State University where I have been teaching reliability engineering and supply chain courses since 2010. The feedback gathered from senior engineering students allowed me to improve and enhance the book content.
I am also very grateful to all the anonymous reviewers who provided constructive suggestions during the early development stage of the book, allowing me to improve and enrich the contents of the book.
My deep appreciations are given to Professor David W. Coit, Professor Elsayed Elsayed, and Professor Hoang Pham at Rutgers University. They taught, supervised, and guided my entry into the reliability engineering world when I was pursuing my graduate study. Since then I have been enjoying this dynamic and fast growing field both in my previous industry appointment and current academic position.
Last, but not least, my thanks are extended to my family members for their support, patience, and understanding during this lengthy endeavor. Special appreciations are reserved for my wife, Youping, who spent tremendous time and effort in taking care of our kid, allowing me to focus on the writing of the book. Without her persevering support this book would not have been available at this moment.
Tongdan Jin
About the Companion Website
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www.wiley.com/go/jin/serviceengineering
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Scan this QR code to visit the companion website
1Basic Reliability Concepts and Models
1.1 Introduction
Reliability is a statistical approach to describing the dependability and the ability of a system or component to function under stated conditions for a specified period of time in the presence of uncertainty. In this chapter, we provide the statistical definition of reliability, and further introduce the concepts of failure rate, hazard rate, bathtub curve, and their relation with the reliability function. We also present several lifetime metrics that are commonly used in industry, such as mean time between failures, mean time to failure, and mean time to repair. For repairable systems, failure intensity rate, mean time between replacements and system availability are the primary reliability measures. The role of line replaceable unit and consumable items in the repairable system is also elaborated. Finally, we discuss the parametric models commonly used for lifetime prediction and failure analysis, which include Bernoulli, binomial, Poisson, exponential, Weibull, normal, lognormal, and gamma distributions. The chapter is concluded with the reliability inference using Bayesian theory and Markov models.
1.2 Reliability Definition and Hazard Rate
1.2.1 Managing Reliability for Product Lifecycle
Reliability engineering is an interdisciplinary field that studies, evaluates, and manages the lifetime performance of components and systems, such as automobile, wind turbines (WTs), aircraft, Internet, medical devices, power system, and radars, among many others (Blischke and Murthy 2000; Chowdhury and Koval 2009). These systems and equipment are widely used in commercial and defense sectors, ranging from manufacturing, energy, transportation, healthcare, communication, and military operations.
The lifecycle of a product typically consist of five phases: design/development, new product introduction, volume shipment, market saturation, and phase‐out. Figure 1.1 depicts the inter‐dependency of five phases. Reliability plays a dual role across the lifecycle of a product: reliability as engineering (RAE) and reliability as services (RASs). RAE encompasses reliability design, reliability growth planning, and warranty and maintenance. RAS concentrates on the planning and management of a repairable inventory system, spare parts supply, and recycling and remanufacturing of end‐of‐life products. RAE and RAS have been studied intensively, but often separately in reliability engineering and operations management communities. The merge of RAE and RAS is driven primarily by the intense global competition, compressed product design cycle, supply chain volatility, environmental sustainability, and changing customer needs. There is a growing trend that RAE and RAS will be seamlessly integrated under the so‐called product‐service system, which offers a bundled reliability solution to the customers. This book aims to present an integrated framework that allows the product manufacturer to develop and market reliable products with low cost from a product's lifecycle perspective.
Figure 1.1The role of reliability in the lifecycle of a product.
In many industries, reliability engineers are affiliated with a quality control group, engineering design team, supply chain logistics, and after‐sales service group. Due to the complexity of a product, reliability engineers often work in a cross‐functional setting in terms of defining the product reliability goal, advising corrective actions, and planning spare parts. When a new product is introduced to the market, the initial reliability could be far below the design target due to infant mortality, variable usage, latent failures, and other uncertainties. Reliability engineers must work with the hardware and software engineers, component purchasing group, manufacturing and operations department, field support and repair technicians, logistics and inventory planners, and marketing team to identify and eliminate the key root causes in a timely, yet cost‐effective manner. Hence, a reliability engineer requires a wide array of skill sets ranging from engineering, physics, mathematics, statistics, and operations research to business management. Last but not the least, a reliability engineer must possess strong communication capability in order to lead initiatives for corrective actions, resolve conflicting goals among different organization units, and make valuable contributions to product design, volume production, and after‐sales support.
1.2.2 Reliability Is a Probabilistic Measure
Reliability is defined as the ability of a system or component to perform its required functions under stated conditions for a specified period of time (Elsayed 2012; O'Connor 2012). It is often measured as a probability of failure or a possibility of availability. Let T be a non‐negative random variable representing the lifetime of a system or component. Then the reliability function, denoted as R(t), is expressed as
It is the probability that T exceeds an expected lifetime t which is typically specified by the manufacturer or customer. For example, in the renewable energy industry, the owner of the solar park would like to know the reliability of the photovoltaic (PV) system at the end of t = 20 years. Then the reliability of the solar photovoltaic system can be expressed as R(20) = P {T > 20}. As another example, as more electric vehicles (EVs) enter the market, the consumers are concerned about the reliability of the battery once the cumulative mileage reaches 100 000 km. In that case, t = 100 000 km and the reliability of the EV battery can be expressed as R(100 000) = P {T > 100 000}. Depending on the actual usage profile, the lifetime T can stand for a product's calendar age, mileage, or charge–recharge cycles (e.g. EV battery). The key elements in the definition of Eq. 1.2.1 are highlighted below.
Reliability is predicted based on “intended function” or “operation” without failure. However, if individual parts are good but the system as a whole does not achieve the intended performance, then it is still classified as a failure. For instance, a solar photovoltaic system has no power output in the night. Therefore, the reliability of energy supply is zero even if solar panels and DC–AC inverters are good.
Reliability is restricted to operation under explicitly defined conditions. It is virtually impossible to design a system for unlimited conditions. An EV will have different operating conditions than a battery‐powered golf car even if they are powered by the same type of battery. The operating condition and surrounding environment must be addressed during design and testing of a new product.
Reliability applies to a specified period of time. This means that any system eventually will fail. Reliability engineering ensures that the system with a specified chance will operate without failure before time
t
.
The relationship between the time‐to‐failure distribution F(t) and the reliability function R(t) is governed by
In statistics, F(t) is also referred to as the cumulative distribution function (CDF). Let f(t) be the probability density function (PDF); the relation between R(t) and f(t) is given as follows:
Example 1.1
High transportation reliability is critical to our society because of increasing mobility of human beings. Between 2008 and 2016 China has built the world's longest high‐speed rail with a total length of 25 000 km. The annual ridership is three billion on average. Since the inception, the cumulative death toll is 40 as of 2016 (Wikipedia 2017). Hence the annual death rate is 40/(2016 − 2008) = 5. The reliability of the ridership is 1 − 5/(3 × 109) = 0.999 999 998. As another example, according to the Aviation Safety Network (ASN 2017), 2016 is the second safest year on record with 325 deaths. Given 3.5 billion passengers flying in the air in that year, the reliability of airplane ridership is 1 − 325/(3.5 × 109) = 0.999 999 91. This example shows that both transportation systems achieve super reliable ridership with eight “9”s for high‐speed rail and seven “9”s in civil aviation.
1.2.3 Failure Rate and Hazard Rate Function
Let t be the start of an interval and Δt be the length of the interval. Given that the system is functioning at time t, the probability that the system will fail in the interval of [t, t + Δt] is
The result is derived based on the Bayes theorem by realizing P{A, B} = P{A}, where A is the event that the system fails in the interval [t, t + Δt] and B is the event that the system survives through t.
The failure rate, denoted as z(t), is defined in a time interval [t, t + Δt] as the probability that a failure per unit time occurs in that interval given that the system has survived up to t. That is,
Although the failure rate z(t) in Eq. 1.2.5 is often thought of as the probability that a failure occurs in a specified interval like [t, t + Δt] given no failure before time t, it is indeed not a probability because z(t) can exceed 1. For instance, given R(t) = 0.5, R(t + Δt) = 0.4, and Δt = 0.1, then z(t) = 2 failures per unit time. Hence, the failure rate represents the frequency with which a system or component fails and is expressed in failures per unit time. The actual failure rate of a product or system is closely related to the operating environment and customer usage (Cai et al. 2011).
Example 1.2
A lithium‐ion battery is a rechargeable energy storage device widely used in electric transportation and utility power storage. The maximum state of charge (SOC) is commonly used to measure the lifetime of a rechargeable battery. The battery fails if the maximum SOC drops below 80% of its initial value. Assume a vehicle operates for 100 000 km and 110 000 km, the probabilities that the maximum SOC of a lithium‐ion battery remains above 80% are 0.95 and 0.9, respectively. According to Eq. 1.2.5, the battery failure rate in [100 000, 110 000] can be estimated as
The hazard function, also known as the instantaneous failure rate, is defined as the limit of the failure rate as Δt approaches zero. It is a rate per unit time similar to reading a car speedometer at a particular instant and seeing 100 km/hour. In the next instant the hazard rate may change and the testing units that have already failed have no impact because only the survivors count. By taking the limit of Δt to zero in Eq. 1.2.5, the hazard rate function h(t) is obtained as follows:
Equation 1.2.6 represents an important result as it governs the relation between h(t), f(t), and R(t). Alternatively, from Eq. 1.2.6, the reliability function R(t) can be expressed as
Let us denote H(t) as the cumulative hazard rate function; then
