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A high percentage of defense systems fail to meet their reliability requirements. This is a serious problem for the U.S. Department of Defense (DOD), as well as the nation. Those systems are not only less likely to successfully carry out their intended missions, but they also could endanger the lives of the operators. Furthermore, reliability failures discovered after deployment can result in costly and strategic delays and the need for expensive redesign, which often limits the tactical situations in which the system can be used. Finally, systems that fail to meet their reliability requirements are much more likely to need additional scheduled and unscheduled maintenance and to need more spare parts and possibly replacement systems, all of which can substantially increase the life-cycle costs of a system. Beginning in 2008, DOD undertook a concerted effort to raise the priority of reliability through greater use of design for reliability techniques, reliability growth testing, and formal reliability growth modeling, by both the contractors and DOD units. To this end, handbooks, guidances, and formal memoranda were revised or newly issued to reduce the frequency of reliability deficiencies for defense systems in operational testing and the effects of those deficiencies. Reliability Growth evaluates these recent changes and, more generally, assesses how current DOD principles and practices could be modified to increase the likelihood that defense systems will satisfy their reliability requirements. This report examines changes to the reliability requirements for proposed systems; defines modern design and testing for reliability; discusses the contractor's role in reliability testing; and summarizes the current state of formal reliability growth modeling. The recommendations of Reliability Growth will improve the reliability of defense systems and protect the health of the valuable personnel who operate them

This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering. The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.

The correct premise of the topic of human reliability is that system personnel fail in their responding just as equipment does, and so they must be considered along with the equipment in the specification of system reliability. The goal of human reliability efforts as they are presently pursued is to find measures of the reliability of human performance that are expressed in the same terms as measures of equipment reliability and can be combined with them to produce system reliability. The thesis of this paper is that conceptualizing human reliability in these terms raises methodological problems that are likely to prevent this goal from being achieved. What is lacking are: a definition of human failure, units of human behavior whose reliability can be determined, a way to synthesize the reliability of larger behavioral sequences from the units if we could specify them, and a way to integrate human reliability, if it could be determined, with equipment reliability. Monte Carlo modeling is seen as a promising approach to the topic.

1 Reliability -- 1.1 Introduction -- 1.2 What is Reliability? Why Want Reliability? Principles of Achieving Reliability -- 1.3 Quantifying Reliability -- 1.4 The Methods of Systems Reliability Synthesis -- 1.5 Need for Reliability Data -- 2 Principles of Reliability Data Bases -- 2.1 Purposes -- 2.2 Construction -- 2.3 Data Acquisition -- 2.4 Physical Implementation -- 2.5 Development and Operation -- 3 Analysis Methodologies -- 3.1 Restrictions Owing to Data Base Assumptions -- 3.2 Constant Fault-Rate or Failure-Rate Methods -- 3.3 Non-Constant Fault-Rate Methods; The Weibull Distribution -- 3.4 More General Data Structures -- 4 Some Achievements Due to the Development of Data Banks -- 4.1 Introduction -- 4.2 The Nuclear Industry -- 4.3 Aircraft Industry -- 4.4 Electronics Industry -- 4.5 Chemical Industry -- 4.6 Data -- 4.7 Computational Considerations -- 4.8 Data Needs or Requirements -- 4.9 Other Factors Worthy of Consideration -- 4.10 Staffing -- 5 Facts: Most Comprehensive Information System For Industrial Safety -- 5.1 Introduction -- 5.2 The TNO Organization -- 5.3 Looking Back on the Start of FACTS -- 5.4 Information Handling -- 5.5 Cause Classification -- 5.6 Data Base Structure -- 5.7 Storage of Original Documents -- 5.8 Applications and the Use of FACTS -- 5.9 New Advances in FACTS -- 5.10 Latest Developments -- 5.11 PC-FACTS -- 6 Reliability Data Collection In Process Plants -- 6.1 General Remarks -- 6.2 Data Collection -- 6.3 Data Treatment and Examples -- 6.4 Uncertainty, Applicability and Caution -- 7 The Centralized Reliability Data Organization (Credo); an Advanced Nuclear Reactor Reliability, Availability, and Maintainability Data Bank and Data Analysis Center -- 7.1 The Basis for CREDO -- 7.2 CREDO—An Historical Perspective -- 7.3 Data Initially Identified for Inclusion -- 7.4 CREDO Component Description and Classification -- 7.5 Design of Data Input -- 7.6 CREDO'S Data Base Management System -- 7.7 Statistical Data Analysis and Processing -- 7.8 CREDO Development Experience -- 7.9 Achievements and Future Directions of CREDO -- 8 The Fabrication Reliability Data Analysis System Dante-QC1 -- 8.1 Introduction -- 8.2 Concept of the DANTE Code System -- 8.3 Data Base Configuration -- 8.4 Processing Function -- 8.5 Application of DANTE for PIE Data Analysis -- 8.6 Future Directions -- 9 Reliability Data Banks at Electricite De France (EDF) -- 9.1 The Origins -- 9.2 History and Objectives of the EDF Data Banks -- 9.3 SRDF -- 9.4 Conclusion -- 10 IAEA's Experience In Compiling A Generic Component Reliability Data Base -- 10.1 Introduction -- 10.2 IAEA's Generic Component Reliability Data Base -- 10.3 Problem Areas Connected with Generic Data Bases -- 10.4 Conclusion -- Appendix: Data Sources Included in the Generic Component Reliability Data Base -- 11 The European Reliability Data System—Erds: Status And Future Developments -- 11.1 Introduction -- 11.2 General Description of ERDS -- 11.3 The Component Event Data Bank (CEDB) -- 12 Development of A Large Data Bank -- 12.1 Introduction -- 12.2 The Data Bank System -- 12.3 Student Collection Scheme -- 12.4 The Item Inventory -- 12.5 Coded Storage -- 12.6 Output Data -- 12.7 Generic Reliability Data Output Enquiry and Reply Service -- 12.8 Reliability Improvements -- 13 Reliability Data Banks—Friend, Foe or A Waste of Time? -- 13.1 Introduction -- 13.2 The Personalities -- 13.3 The DataBase Design -- 13.4 The Component Inventory Data -- 13.5 The Component History Data -- 13.6 Dependent Failures -- 13.7 Data Analysis -- 13.8 Pooled Reliability Data -- 13.9 The Successes -- 13.10 Conclusions -- 14 Developments -- 14.1 Introduction -- 14.2 Changes in Data Handling -- 14.3 Data Base Software -- 14.4 Methodology and Technology Led Changes -- 14.5 New Data Bases -- 14.6 R & M 2000 -- 14.7 Changes in Attitude -- 15 Overview; Into the Future -- 15.1 Forty Years of Always Being Wrong and Always Being Right -- 15.2 The Next Forty Years -- 15.3 Using External Data Sources and Making Up Data -- 15.4 Justifying a Reliability Data Base.