Reliability Prediction and Testing Textbook E-Book

Lev M. Klyatis

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 351

Veröffentlichungsjahr: 2018

Beschreibung

This textbook reviews the methodologies of reliability prediction as currently used in industries such as electronics, automotive, aircraft, aerospace, off-highway, farm machinery, and others. It then discusses why these are not successful; and, presents methods developed by the authors for obtaining accurate information for successful prediction. The approach is founded on approaches that accurately duplicate the real world use of the product. Their approach is based on two fundamental components needed for successful reliability prediction; first, the methodology necessary; and, second, use of accelerated reliability and durability testing as a source of the necessary data.

Applicable to all areas of engineering, this textbook details the newest techniques and tools to achieve successful reliabilityprediction and testing. It demonstrates practical examples of the implementation of the approaches described. This book is a tool for engineers, managers, researchers, in industry, teachers, and students. The reader will learn the importance of the interactions of the influencing factors and the interconnections of safety and human factors in product prediction and testing.

Leseprobe

Table of Contents

Cover

Copyright

Dedication

Preface

References

About the Authors

Edward L. Anderson

Introduction

What is reliability?

Who gains from improved reliability?

Chapter 1: Analysis of Current Practices in Reliability Prediction

1.1 Overview of Current Situation in Methodological Aspects of Reliability Prediction

1.2 Current Situation in Practical Reliability Prediction

1.3 From History of Reliability Prediction Development

1.4 Why Reliability Prediction is Not Effectively Utilized in Industry

References

Exercises

Chapter 2: Successful Reliability Prediction for Industry

2.1 Introduction

2.2 Step‐by‐Step Solution for Practical Successful Reliability Prediction

2.3 Successful Reliability Prediction Strategy

2.4 The Role of Accurate Definitions in Successful Reliability Prediction: Basic Definitions

2.5 Successful Reliability Prediction Methodology

References

Exercises

Chapter 3: Testing as a Source of Initial Information for Successful Practical Reliability Prediction

3.1 How the Testing Strategy Impacts the Level of Reliability Prediction

3.2 The Role of Field Influences on Accurate Simulation

3.3 Basic Concepts of Accelerated Reliability and Durability Testing Technology

3.4 Why Separate Simulation of Input Influences is not Effective in Accelerated Reliability and Durability Testing

References

Exercises

Chapter 4: Implementation of Successful Reliability Testing and Prediction

4.1 Direct Implementation: Financial Results

4.2 Standardization as a Factor in the Implementation of Reliability Testing and Prediction

4.3 Implementing Reliability Testing and Prediction through Presentations, Publications, Networking as Chat with the Experts, Boards, Seminars, Workshops/Symposiums Over the World

4.4 Implementation of Reliability Prediction and Testing through Citations and Book Reviews of Lev Klyatis's Work Around the World

4.5 Why Successful Product Prediction Reliability has not been Widely Embraced by Industry

References

Exercises

Chapter 5: Reliability and Maintainability Issues with Low‐Volume, Custom, and Special‐Purpose Vehicles and Equipment

5.1 Introduction

5.2 Characteristics of Low‐Volume, Custom, and Special‐Purpose Vehicles and Equipment

References

Exercises

Chapter 6: Exemplary Models of Programs and Illustrations for Professional Learning in Reliability Prediction and Accelerated Reliability Testing

6.1 Examples of the Program

6.2 Illustrations for these and Other Programs in Reliability Prediction and Testing

Index

End User License Agreement

List of Tables

Chapter 02

Table 2.1 The results of short field testing of prototypes of self‐propelled spraying machines.

Table 2.2 Testing results of prototypes of the studied machines.

Table 2.3 Normalized coefficients corresponding with the most important manufacturing and field factors.

Table 2.4 Unknown parameters

α

i

and

α

ij

.

Table 2.5 Coefficient of recalculating for the machines studied.

Table 2.6 Predicted mean time to failure.

Chapter 04

Table 4.1 Comparison of the complaints for a reason [1].

Table 4.2 Example of practical economic results of the proposed approach for tools [1] by one company.

List of Illustrations

Chapter 01

Figure 1.1 Reliability as one from interacted performance components in the real world.

Figure 1.2 Common scheme of company's vice‐president's sectors of responsibility. 1: one vice‐president's area; 2: second vice‐president's area; 3: third vice‐president's area; 4: fourth vice‐president's area; 3a: area of responsibilities of director of first department; 3b: area of responsibilities of director of second department; 3c: area of responsibilities of director of third department.

Figure 1.3 The reasons why accelerated stress testing cannot provide information for successful reliability prediction.

Chapter 02

Figure 2.1 Depiction of the step‐by‐step solution for practical successful reliability prediction.

Figure 2.2 Common scheme of successful reliability prediction strategy.

Figure 2.3 Five common steps for successful reliability prediction.

Figure 2.4 Interacted groups of real world conditions for the product/process.

Figure 2.5 Common scheme of methodology for product's reliability successful prediction.

Figure 2.6 Evaluation of the correspondence between functions of distribution of the time to failure of a car trailer's transmission details in the field and in the ART/ADT conditions.

Chapter 03

Figure 3.1 Scheme of the two basic aspects of testing.

Figure 3.2 Scheme of the four basic approaches to accelerated testing.

Figure 3.3 The path from traditional ALT with separate (or some) simulation input influences to ART/ADT with simulation for the full field situation (full field input influences plus safety plus human factors).

Figure 3.4 Reasons why the currently used approaches to accelerated stress testing often lead to unsuccessful prediction of reliability and durability.

Figure 3.5 Ratio of different areas of testing.

Figure 3.6 Progress of the different areas of activity (over the last 50–60 years).

Figure 3.7 The path from field input influences to failures.

Figure 3.8 Factors in the vibration of a mobile test subject in the field.

Figure 3.9 Principal scheme of corrosion in the field as a result of multi‐environmental and mechanical influences, and their interactions.

Figure 3.10 Depiction of the various input influences that must be accounted for based on actual field conditions experienced by the product.

Figure 3.11 Scheme of the study of temperature as an example of input influence on the test subject.

Figure 3.12 The full hierarchy of the complete product and its components as a test subject.

Figure 3.13 The two basic components of ART/ADT.

Figure 3.14 Basic components of accelerated laboratory testing as a component of ART/ADT.

Figure 3.15 Periodic field testing as the second major component of ART/ADT.

Figure 3.16 Demonstration of the influence of management and operator's reliability on the product/technology reliability [2].

Figure 3.17 Schematic trends in development of physical simulation of the real‐world conditions and ART/ADT.

Figure 3.18 Some reasons for low engineering culture.

Figure 3.19 Interconnected group of real‐world input influences on test subject.

Figure 3.20 Multi‐environmental group of input influences.

Figure 3.21 Example of content of interacted components of mechanical group of input influences.

Chapter 04

Figure 4.1 Normalized correlation

ρ

(

τ

) and power spectrum

S

(

ω

) of car trailer's frame tension data during ART and field testing (test result from one sensor).

Figure 4.2 Dr. Lev Klyatis, chairman of State Enterprise Testmash and full Professor Moscow University of Agricultural Engineers, in the test center, where implemented his ideas for ART development of farm machinery (1990).

Figure 4.3 Six‐axis vibration test equipment (Testmash, Russia), as a component of ART/ADT. Implemented in Zelenograd Electronic Center, Moscow State (Russia) (1991).

Figure 4.4 Change in the engine complaints for 3 years after implementation of new approach to ART and reliability prediction [1].

Figure 4.5 Increased volume of sales of instruments after the implementation of the new approaches, described in Chapters and [1].

Figure 4.6 Dr. Yakhya Abdulgalimov (Testmash) during implementation component of ART/ADT in industrial company Selmash (Bobruysk, Belorussia).

Figure 4.7 Final real results of successful prediction of product reliability.

Figure 4.8 Plan of test chamber (Testmash design) for completed truck. Kamaz Inc. (Russia). Engineering Center, Block No. 3.

Figure 4.9 The letter from ASAE T‐14 Committee that Lev Klyatis should be listed as the coordinator of the project “Rewriting the Standard EP 456 Test and Reliability Guidelines.”

Figure 4.10 The first page of the third draft of the standard EP 456 “Test and Reliability Guidelines.”

Figure 4.11 The ballot for the ASAE standard EP 456 (Lev Klyatis, project leader).

Figure 4.12 Lev Klyatis (second from left) with group of experts from SAE International, G‐11 Division in NASA Langley Research Center, NASA.

Figure 4.13 SAE G‐11 Division members in standardization of reliability and maintainability in aerospace, during a meeting in Washington, DC. Lev Klyatis is fourth from the left.

4.14a General components of reliability testing technology. SAE International Reliability Testing Standard JA1009/1.

4.14b Scheme of input influences and output variables of the actual product.

4.14c Types of physics‐of‐degradation mechanisms and their parameters.

4.14d Scheme of ART/ADT.

4.14e Scheme of special field testing.

4.14f The factors that influence the product's reliability, safety, and quality through operator's and management reliability and quality.

Figure 4.15Figure 4.15 Meeting TC‐56. Lev Klyatis is second (right) from chair.

Figure 4.16 The letter from General Secretary of United States National Committee for IEC about accreditation Lev Klyatis as US Representative for IEC.

Figure 4.17 Lev Klyatis expert of the USA Technical Advisory Group for the IEC in Sydney (Australia) during the meeting.

Figure 4.18 Dr. Lev Klyatis receiving an award from China (Beijing) during the IEC Congress.

Figure 4.19 These documents validate Lev Klyatis as an Expert of ISO/IEC Joint Study Group in Safety Aspects of Risk Assessment.

Figure 4.20 Documents demonstrating Lev Klyatis as an Expert of ISO/IEC Joint Study Group in Safety Aspects of Risk Assessment.

Figure 4.21 Dr. Lev Klyatis during his lecture for professionals in reliability testing and prediction (Latvia, 1974).

Figure 4.22 Cover page and first page of Chapter 6 from Dr. Lev Klyatis's paper for the United Nations.

Figure 4.23 First page of published interview with Dr. Lev Klyatis, Chairman Testmash.

Figure 4.24 First job for Professor Klyatis in the USA: fish delivery.

Figure 4.25 Published review in the journal

Total Quality Management and Business Excellence

, Taylor & Francis Group, Volume 17, Number 7, September 2006, UK.

Figure 4.26 Front cover of the book [1].

Figure 4.27 The first day's program of the DoD, Department of Transportation, and industry workshop/symposium.

Figure 4.28 Lev Klyatis, chairman of technical session IDM300 Trends in Development Accelerated Reliability and Durability Testing Technology, SAE 2014 World Congress, introducing a speaker from Jatko Ltd (Japan).

Figure 4.29 The system of drive‐in test chamber (Advanced Center of Excellent, University of Toronto, Canada).

Figure 4.30 E‐mail response from ACE (Canada) to Lev Klyatis's invitation for presentation of an ACE solution at the SAE World Congress.

Figure 4.31 Front cover of the book published by SAE International.

Figure 4.32 Lev Klyatis, presenter at the RAMS. Paul Parker is a chair of the technical session.

Figure 4.33 Lev Klyatis, panel presenter at the IEEE Workshop on Accelerated Stress Testing, Pasadena, CA, 1998.

Figure 4.34 The title page of the visuals during the presentation at the IEEE ASTR Workshop, 2009.

Figure 4.35 One of the visuals from the presentation in Figure 4.34.

Figure 4.36 Elmer Sperry Award ceremony during SAE 2012 World Congress (Detroit). From left: SAE International President, Elmer Sperry Board of Award Chairman Richard Miles, professor Princeton University, Dr. Lev Klyatis, this award sponsor, award recipients Dr. H. Hecht and Dr. Zigmund Bluvband.

Figure 4.37 Group of recipients and sponsors of Elmer Sperry award for Apollo–Soyuz project during NASA Award Ceremony (Washington, DC). From left: Glynn Lunney, chair of Apollo project; Richard Miles, co‐sponsor of award, professor Princeton University, Elmer Sperry Board of award member; General Thomas Stafford, chair of Apollo team; Lev Klyatis, co‐sponsor of award, Elmer Sperry Board of award member.

Figure 4.38 Lev Klyatis in the National Air and Space Museum in Washington, DC, in front of the joined Apollo–Soyuz spacecraft (left is Apollo, right is Soyuz).

Figure 4.39 Meeting announcement for Dr. Klyatis presentation for engineers and managers of two societies in New York: SAE International Metropolitan section and NAFA's New York intercounty chapter.

Figure 4.40 SAE 2017 World Congress (WCX17), April 4, Detroit. Lev Klyatis (IDM300 technical session Chairman) hands Certificate In Recognition for Speaker Obuli Karthikeyan (Deputy Manager Component Test Lab, Ashok Leyland, India).

Figure 4.41 Book review, published in

The Journal of RMS (Reliability, Maintainability, and Supportability) in Systems Engineering

, DoD, Winter 2007/2008.

Chapter 05

Figure 5.1 Typical airport snow and ice control equipment.

Figure 5.2 Emergency generator rooftop installation.

Figure 5.3 Aircraft fueling cart (tow type).

Figure 5.4 Typical airport runway deicer vehicle.

Figure 5.5 Elevating platform truck.

Figure 5.6 Emergency fire pump.

Figure 5.7 Author overseeing SAE ARP 5539 snow blower performance testing [3].

Figure 5.8 National Fire Protection Association 414 ARFF tilt table testing [1].

Figure 5.9 Bus lost due to fire.

Figure 5.10 Diesel injector line hole caused by chafing.

Figure 5.11 Wiring harnesses rerouted to clear injector line.

Figure 5.12 This author witnessing prototype wrecker turn around functional testing.

Figure 5.13 This author supervising snow blower in‐cab sound level acceptance testing.

Chapter 06

Figure 6.1 Introduction.

Figure 6.2 Current situation with product reliability.

Figure 6.3 Brigadier General Carl Schenk described.*

Figure 6.4 There are many other examples with recalls.

Figure 6.5 Highest recalls during last year.*

Figure 6.6 Not only recalls, but.

Figure 6.7 One from final result of inaccurate prediction is.

Figure 6.8 It is real fact that.

Figure 6.9 Ways for finding the causes for complaints and recalls.

Figure 6.10 Example: results of saving expenses for testing during design and manufacturing.

Figure 6.11 Statistical criteria for comparison the reliability in results of accelerated reliability testing (ART) and field testing.

Figure 6.12 Statistical criteria … (continuation).

Figure 6.13 Comparison parameter's function with predetermined accuracy and confidence area.

Figure 6.14 Axiom of stress testing.

Figure 6.15

Figure 6.16

Figure 6.17

Figure 6.18

Figure 6.19

Figure 6.20

Figure 6.21

Figure 6.22

Figure 6.23

Figure 6.24

Figure 6.25

Figure 6.26

Figure 6.27

Figure 6.28

Figure 6.29 Four basic steps for successful reliability prediction.

Figure 6.30 Recalls of automobiles from 1990 to 2004 (millions) in the American market.

Figure 6.31 Examples of separate types of practical simulation and stress testing during design and manufacturing. This is low effective way.

Figure 6.32 Basic reasons why accelerated stress testing cannot help to accurately predict reliability and durability.

Figure 6.33 Basic components of ART/ADT.

Figure 6.34 Example of periodic field testing.

Figure 6.35 Example of interacted (simultaneous combination) of the real‐world input influences on the product.

Figure 6.36 The way from actual field input influences to failures (or degradation only).

Figure 6.37 Example: The types and parameters of the degradation mechanisms.

Figure 6.38 The way to reliability/durability testing.

Figure 6.39 Contents of ART/ADT technology.

Figure 6.40 Scheme of study the temperature as an example of accurate simulation input influences.

Figure 6.41 Accelerated destruction of paint protection in test chamber (two types of paint).

Figure 6.42 Dependence of steel corrosion values on the number of wettings in test chamber.

Figure 6.43 Vibration in test certification process in aircraft. The 190 Aircraft and vibration equipment.

Figure 6.44 The system (test subject) as complex of interconnected components (units and details).

Figure 6.45 Different types of mechanical testing.

Figure 6.46 Technology vibration of mobile product in the field.

Figure 6.47 Stages of basic vibration testing equipment development.

Figure 6.48 Climate test chamber with four‐wheel‐drive dynamometer with sunlight simulation (Weiss Technik).

Figure 6.49 Cold‐head climate test chamber with road simulator and sunlight simulator (Weiss Technik).

Figure 6.50 Combined testing system: vibration, climate, and corrosion (Weiss Technik).

Figure 6.51 Combined test chamber for electronic devices. Simulates vibration, temperature, input voltage, and humidity.

Figure 6.52 Bus climatic wind tunnel.

Figure 6.53 Bus climatic wind tunnel: specifications.

Figure 6.54Figure Normalized correlation and power spectrum of frame tension data for the car's trailer in different field conditions and in the chamber.

Figure 6.55 Deformation of metallic sample during the time in the field and during ART/ADT.

Figure 6.56 Effect of poor reliability on profit.

Figure 6.57 Scheme of complex analysis of factors that influence product reliability/quality.

Chapter 04

Figure 1 Total number of automotive recalls in the USA in 1980–2013 [11] (vertical line is percent, if the number of recalls is equivalent 100% in 1980, in 2010 number of recalls in percentage was approximately 500%).

Guide

Cover

Table of Contents

Begin Reading

Pages

C1

iv

v

xi

xii

xiii

xiv

xv

xvi

xvii

xix

xx

xxi

xxiii

xxiv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

217

218

219

220

221

222

243

244

245

246

Reliability Prediction and Testing Textbook

Lev M. KlyatisProfessor Emeritus

Habilitated Dr.-Ing., Dr. of Technical Sciences, PhD

Edward L. Anderson

BS in Mechanical Engineering

Copyright

This edition first published 2018

© 2018 John Wiley & Sons, Inc.

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 Lev M. Klyatis and Edward L. Anderson to be identified as the authors of this work has been asserted in accordance with law.

Registered Office

John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

Editorial Office

111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by print–on–demand. Some content that appears in standard print versions of this book may not be available in other formats.

Limit of Liability/Disclaimer of Warranty

The publisher and the authors make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties; including without limitation any implied warranties of fitness for a particular purpose. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for every situation. In view of on–going research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. The fact that an organization or website is referred to in this work as a citation and/or potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. No warranty may be created or extended by any promotional statements for this work. Neither the publisher nor the author shall be liable for any damages arising here from.

Library of Congress Cataloging–in–Publication Data

Names: Klyatis, Lev M., author. | Anderson, Edward L., 1945– editor.

Title: Reliability prediction and testing textbook / by Lev M. Klyatis ;

Edward L. Anderson, language editor.

Description: Hoboken, NJ, USA : Wiley, 2018. | Includes bibliographical

references and index. |

Identifiers: LCCN 2017054872 (print) | LCCN 2017059378 (ebook) | ISBN

9781119411925 (pdf) | ISBN 9781119411932 (epub) | ISBN 9781119411888

(cloth)

Subjects: LCSH: Accelerated life testing.

Classification: LCC TA169.3 (ebook) | LCC TA169.3 .K5964 2018 (print) | DDC

620/.00452–dc23

LC record available at https://lccn.loc.gov/2017054872

Cover Design: Wiley

Cover Image: © naqiewei/Getty Images

Dedication

To my wife Nellya Klyatis

To my wife Carol Anderson

Preface

Lev M. Klyatis and Edward L. Anderson

When Lev Klyatis began his engineering career in 1958 as a test engineer at the Ukrainian State Test Center for farm machinery, he was surprised to learn that, even after extensive testing by this center, the testing was not accurately predicting the reliability of the products as used by farmers. This test center would conduct farm machinery field testing during one season of operation, and make the recommendation to manufacture the new product based on results of this single‐season testing.

Neither the designers, nor test engineers, nor the researchers, nor other decision‐makers involved knew what would happen after the first season. The test center was not accurately predicting true product reliability during the life cycle of the machines. Later, Lev Klyatis realized that this situation was not unique to farm machinery, but was related to other areas of industry and other countries over the world, even when they claimed to be doing accelerated reliability testing.

Why are we writing this book? As will be seen, it is the author's observation that the developments of technology, methodologies, hardware, and software are advancing at an unprecedented rate. But, in the same time, we find that reliability testing and prediction are advancing much more slowly; and in many cases it is common to find reliability testing and prediction methodologies that have changed little in the past 60–70 years. As product complexity increases, the need for near‐perfect product reliability, which is founded on the ability to accurately predict reliability prior to widespread production and marketing, becomes a company's critical objective. Failure to predict and remedy failures can result in human tragedy, as well as serious financial losses to the company. Consider the two following recent examples.

On May 31, 2009, Air France's flight AF447 departed Rio de Janeiro en route to Paris carrying 228 passengers and crew; several hours into the flight it crashed into the Atlantic Ocean, killing all on board [1,2]. A contributing factor in the accident was pitot tubes, which were believed to have iced, resulting in the loss of accurate airspeed and altitude information. The pitot tubes were known to have a problem with icing and had been replaced by several other airlines. Following the accident, The European Aviation Safety Agency (EASA) made compulsory the replacement of two out of three airspeed pitot's on Airbus A330s and A340s AD (204‐03‐33 Airbus Amendment 3913‐447. Docked 2001‐ NM‐302‐AD), and the FAA followed with a near‐identical requirement in promulgating Docket No. FAA‐2009‐0781 AD 2009‐18‐08 Final Rule Airworthiness Directive AD concerning Airbus A330 and A340 airplanes. It is profoundly troubling that in age of state‐of‐the‐art fly‐by‐wire jet aircraft, we would be encountering problems with pitot tube icing [3].

In February 2014, General Motors issued a recall for over 2.6 million vehicles to correct an ignition switch defect responsible for at least 13 deaths, and possible more than 100, and this does not include those seriously injured. The ignition switch could move from the “On” position to the “Acc” position; and, when this happened, safety systems, such as air bags, anti‐lock brakes, and power steering, could be disabled with the vehicle moving. The problem was initially uncovered by GM as early as 2001, with continued recommendations to change the design through 2005, but this recommendation was rejected by management.

By the end of March of 2015 the cost to GM for the ignition switch recalls was $200 million and was expected to reach as much as $600 million [4–6]. Add to the financial loss the personal tragedy of those killed or injured and to their families, and the true cost of failed reliability prediction becomes evident. By the end of the next decade it is almost a certainty that you will be sharing the road with some type of autonomous vehicle [7–9]. Consider the degree of reliability prediction that will be needed to provide the level of confidence needed. Whether you are driving an autonomous vehicle or merely sharing the road with them, you are literally betting your life on the adequacy and accuracy of the reliability testing for each critical component and decision‐making process. Considering that, today, we are having difficulties with ignition switches and pitot tubes, this will be a major undertaking.

This is particularly so when the testing will need to account for such varied environmental conditions as heat, cold, rain, snow, roadway salt, and various other expected and unexpected contaminants. Couple this with the 10 years plus life of the average automobile [10], and reliability assurance against a wide variety of degradations is necessary, and all life failure modes must default to a fail‐safe mode. These are only examples from many real‐life problems that are connected with inadequate reliability prediction and testing methods.

Unfortunately, too often these costs for failed reliability prediction and testing are never factored into an organization's decision‐making processes. While the human and financial impacts of responsible new product development should be foremost in an organization's (including research and pre‐design, and testing) activities and concerns, too often they are overlooked or assumed to be someone else's responsibility, especially in a large organization. But if we are to remain a civilized society, such responsibility cannot and should not be delegated up the chain of command.

One of the major concerns is the development of higher speed and processing power of electronic developments occurring at a much greater rate than other areas of people's activity. In fact, Moore's law—the expectation that microprocessor power doubles every 18 months—is widely accepted in the industry. How often do we consider the effects and the implications of electronic developments, especially in new software development, that transfers thinking resulting in real brain development to what may be termed virtual thinking? Virtual thinking is surrendering human thinking and mental development to a system of control that provides answers automatically, with a minimal role for thought. Consider how calculators and electronic cash registers have reduced people's ability to do basic mathematics; or how GPS navigation has diminished the average person's ability to read a map and plot the course to their destination—it is so much easier to just type in the destination address and let the machine direct you turn by turn to the destination. But the development of thinking skills and using them for the advancement of society and civilization are the basic differences between humans and animals.

Unfortunately, people often do not understand that electronic systems are only part of a system of controls containing real physical limits, processes and technologies, and that there are limits to what can be accomplished with software and corresponding hardware. The most advanced automotive stability system cannot allow the vehicle to corner at an unsafe speed. While the system may enhances a person's abilities as a driver, it cannot violate the rules of physics. And frequently, the enhancement provided by technology is accompanied by a reduction in the skill of the operator as they become increasingly dependent on the technology.

A common example of this occurs when predictions are based on abstract (virtual) processes which are different than the real (actual) processes. Too often, prediction reliability is based on only virtual (theoretical) understanding and does not account for the real situations in people's real life. Because of this, many reliability prediction approaches are based only on theoretical knowledge, and the testing used is not a real interconnected process, but relies on secondary conditions expected in their virtual world. Therefore, testing development, together with prediction development, is not developing as quickly as needed and is moving forward very slowly, much more slowly than design and manufacturing processes (Figure 3.6). Reliability (mostly accelerated reliability) testing needs technology, equipment, and corresponding costs as complicated as the new products they are testing. But too often should be key concerns the management of many companies prefer to pay as little as they can for this technology and the development of necessary testing equipment. They want to save expenses for this stage of product development. Phillip Coyle, the former director of the Operational Test and Evaluation Office (Pentagon) said in the US Senate that if, during the design and manufacturing of complicated apparatus such as a satellite, one tries to save a few pennies in testing, the end results may be a huge loss of thousands of dollars due to faulty products which have to be replaced because of this mistake. This relates to other products, too.

As a result, product reliability prediction is unsuccessful, which is reflected in increasing and unpredictable recalls, decreasing actual reliability of industrial products and technologies, decreasing profit and increasing life‐cycle cost, in comparison with that planned during design. Lev Klyatis came to realize that the reliability prediction approaches utilized at the time (and even frequently now) did not obtain accurate or adequate initial information to successfully predict the reliability of the machinery in actual use. There was a need for more extensive and accurate testing prior to manufacturing if long‐term field reliability was a desired outcome.

Testing needed to account for multiple seasons, long life, varying operational conditions, and other factors if the machinery was to perform satisfactorily. This meant a refocusing of the accelerated testing (laboratory, proving grounds, field intensive use), initially for farm machinery and then for other products and technological systems. Without accurate simulation of real‐ world conditions, test results may be very different from real‐world results. If one thinks that the approach of simply recalculating proving ground (or any other simple simulation of real life) results is all that is needed to provide real world simulation, they will be disappointed. If the initial information used in testing (testing protocols) is not correct, the prediction method will not be useful in practice. But, it also became apparent that reliability prediction approaches were mostly theoretical in nature, because of the difficulty of accurately simulating the real‐world situation.

Much of this continues even now. As a result, companies design, begin manufacturing, and have the product in the hands of the customers only to discover reliability issues that result in serious consequences—usually economic losses, but increasingly legal consequences. And, all too often, it is the consumer who suffers from the poor reliability prediction. As an example, Figure 1 shows automotive recalls for the years between 1980 and 2013. While automobile technology is relatively mature, product complexity and technology changes are resulting in significant amounts of recalls, which is indicative of testing adequacy moving too slowly (see Figure 3.6).

Figure 1 Total number of automotive recalls in the USA in 1980–2013 [11] (vertical line is percent, if the number of recalls is equivalent 100% in 1980, in 2010 number of recalls in percentage was approximately 500%).

Source: National Highway Traffic Safety Administration.

This situation continues. For example [12]:

In September (2016), Ford expanded its recall on hundreds of thousands of Ford and Lincoln vehicles early in the year to a staggering 2,383,292 vehicles. The problem involves a side door‐latching component, which results in the door not closing or latching properly. The door will either not close at all, or the door could close temporarily, only to reopen later while the vehicle is in motion. This, of course, would be a terrifying thing to encounter and severely increases the risk of injury. Needless to say, Ford and Lincoln dealers will replace the side door latches at no cost to the consumer.

But the question remains: Why should we be having problems with something as basic as a door latching system? And [13]:

Since the beginning of the year, Toyota has recalled nearly 3 million RAV4s and RAV4 EVs after evidence emerged that there was an issue with the second‐row safety belts. The problem involves how the seat belt interacts with the metal frame of the seat cushion, and, in the event of an accident, the metal frame could cut the seat belt right in two. As a result the seat belt fails at its most fundamental function. In order to alleviate this issue, Toyota simply adds a cover to the metal seat cushion frame that prevents it from cutting through the belt in a collision.

And again, the question is posed: Why was this not discovered in testing? In 2017, Reuters informed [14]:

The U.S. Transportation Department said automakers recalled a record high 53.2 million vehicles in 2016 in the United States in part because of a massive expansion in call back to replace Takata Corp 7312.T air bag inflators.

Under aggressive enforcement …, automakers issued a record‐setting 927 recall campaigns, up 7 percent over the previous high set in 2015. Last year's recall of 53.2 million total vehicles topped the previous all‐time high of 51.1 million set in 2015, the department said.

While there are many publications discussing automotive and other industrial product recalls, most of them focus on the reliability and safety aspects of these recalls, and the financial impacts to the parties involved. But reliability and safety problems are not the true causes for these recalls. It is most often the result of faulty reliability prediction.

The low level of reliability prediction can lead to deaths or injuries as a result of incidents, and ultimately result in increasing cost of the product, decreasing the company's profit and image, incurring losses to the customers, and many other problems. In some cases, low‐level reliability prediction can even result in criminal prosecution of the company's leaders or staff. These problems are considered in greater detail in the book Successful Prediction of Product Performance[11]. Finally, one should not forget that poor product reliability is connected to other performance factors, such as durability, maintainability, safety, life‐cycle cost, profit, and others. In the real world, these are often interconnected and interact with each other.

It is the purpose of this textbook to provide guidance on how one can improve reliability prediction and testing as two interconnected components with corresponding close development for both.

Lev M. Klyatis

Edward L. Anderson

References

1 AirSafe.com, LLC. (2011).

Air France A330 crash in the Atlantic Ocean

.

http://www.airsafe.com/plane‐crash/air‐france‐flight‐447‐airbus‐a330‐atlantic‐ocean.htm

(accessed February 5, 2018).

2 Traufett G. (2010).

The last four minutes of Air France Flight 447

.

http://www.spiegel.de/international/world/death‐in‐the‐atlantic‐the‐last‐four‐minutes‐of‐air‐france‐flight‐447‐a‐679980.html

(accessed February 5, 2018).

3 The Associated Press. (2009).

Airspeed sensor troubled history ignored?

https://www.cbsnews.com/news/airspeed‐sensor‐troubled‐history‐ignored/

(accessed February 5, 2018).

4 Shepardson D, Burden M. (2015).

GM to pay $900M to resolve ignition switch pro

be.

http://www.detroitnews.com/story/business/autos/general‐motors/2015/09/16/gm‐billion‐fine‐resolve‐ignition‐switch‐probe/32526023/

(accessed February 5, 2018).

5 Valcies‐Dapena P, Yellin T. (2017).

GM: Steps to a recall nightmare

.

http://money.cnn.com/infographic/pf/autos/gm‐recall‐timeline/index.html

(accessed February 5, 2018).

6 NastLaw, LLC (n.d.).

General Motors recall timeline

.

https://nastlaw.com/general‐motors‐recall/timeline/

(accessed February 5, 2018).

7 BI Intelligence. (2016).

10 million self‐driving cars will be on the road by 2020

.

http://uk.businessinsider.com/report‐10‐million‐self‐driving‐cars‐will‐be‐on‐the‐road‐by‐2020‐2015‐5‐6

(accessed February 5, 2018).

8 Insurance Journal. (2015).

How the age of autonomous vehicles will evolve

.

http://www.insurancejournal.com/news/national/2015/04/24/365573.htm

(accessed February 5, 2018).

9 Muller J. (2015).

The road to self‐driving cars: a timeline

.

https://www.forbes.com/sites/joannmuller/2015/10/15/the‐road‐to‐self‐driving‐cars‐a‐timeline

(accessed February 5, 2018).

10 Rechtin M. (2013).

Average age of U.S. car, light truck on road hits record 11.4 years, Polk says

.

http://www.autonews.com/article/20130806/RETAIL/130809922/average‐age‐of‐u.s.‐car‐light‐truck‐on‐road‐hits‐record‐11.4‐years

(accessed February 5, 2018).

11 Klyatis L. (2016).

Successful Prediction of Product Performance

.

Quality, Reliability, Durability, Safety, Maintainability, Lifecycle Cost, Profit, and Other Components

. SAE International.

12 Boudette NE, Kachi H. (2014).

Big car makers in race to recall

.

https://www.wsj.com/articles/toyota‐recalls‐6‐4‐million‐vehicles‐worldwide‐1397025402

(accessed February 5, 2018).

13 Harrington J. (2016).

Total recall: 5 of the most expansive automotive recalls of 2016

.

https://blog.cargurus.com/2016/11/04/5‐of‐the‐most‐expansive‐automotive‐recalls‐of‐2016

(accessed February 5, 2018).

14 Shepardson D, Paul F. (2017).

U.S. auto recalls hit record high 53.2 million in 2016

.

https://uk.reuters.com/article/us‐usa‐autos‐recall/u‐s‐auto‐recalls‐hit‐record‐high‐53‐2‐million‐in‐2016‐idUKKBN16H27A

(accessed February 5, 2018).

About the Authors

Lev M. Klyatis

Lev Klyatis is a senior adviser at SoHaR, Incorporated. He holds three doctoral degrees: engineering technology, PhD; engineering technology, ScD (a high‐level East European doctoral degree); and engineering, Habilitated Dr.‐Ing. (a high‐level west European doctoral degree).

His major scientific/technical expertise has been in the development of new directions for the successful prediction of product performance, including reliability during service life (or other specified time), and accelerated reliability and durability testing with accurate physical simulation of field conditions. Dr. Klyatis developed new ideas and unique approaches to accurate simulation. This approach was founded on the integration of field inputs, safety, and human factors, improvement in the engineering culture, and accelerated product development. He developed a methodology for reducing complaints and recalls. His approach has been verified in various industries, primarily automotive, farm machinery, airspace, and aircraft. He has served as a consultant to Ford, DaimlerChrysler, Nissan, Toyota, Jatco Ltd, Thermo King, Black and Decker, NASA Research Centers, Carl Schenk (Germany), and many others.

He was qualified as a full professor by the USSR's Highest Examination Board and was a full professor at the Moscow University of Agricultural Engineers. He has served on the US–USSR Trade and Economic Council, the United Nations Economic Commission for Europe, and the International Electrotechnical Commission (IEC). He also served as an expert of the United States, and an expert of the International Standardization Organization and International Electrotechnical Commission (ISO/IEC) Joint Study Group in Safety Aspects of Risk Assessment. He was the research leader and chairman of the State Enterprise Testmash, Moscow, Russia, and principal engineer of a state test center. He is presently a member of the World Quality Council, the Elmer A. Sperry Board of Award, SAE International G‐11 Reliability Committee, the Integrated Design and Manufacturing Committee of SAE International World Congresses, Session Chairman for SAE World Congresses in Detroit since 2012, and a member of the Governing Board of SAE Metropolitan Section. He has been a seminar instructor for the American Society for Quality.

Lev Klyatis is the author of over 250 publications, including 12 books, and holds more than 30 patents worldwide. Dr. Klyatis frequently speaks at technical and scientific events that are held around the world.

Edward L. Anderson

Edward Anderson is a professional engineer with over 40 years' experience in the design, procurement, and operation of highly specialized automotive vehicles. He is active in the engineering profession in SAE International, the Sperry Board of Award, and the SAE G‐15 Airport Ice and Snow Control Equipment Committee, of which he was a founding member. Ed studied engineering at Newark College of Engineering, graduating with a BS in mechanical engineering and a commission in the US Air Force. He served as an Air Force pilot for 5 years, logging over 1000 hours flying time in C‐141 Starlifter four‐engine turbojet transports flying global airlift missions, and several hundred hours in HH43 Huskie rescue helicopters.