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Abstract

This chapter considers in detail the concepts of reliability and performance in engineering design, as well as the various criteria essential to designing for reliability. Reliability in engineering design may be considered from the points of view of whether a design has inherently obtained certain attributes of functionality, brought about by the properties of the components of the design, or whether the design has been configured at systems level to meet certain operational constraints based on specific design criteria. Designing for reliability includes all aspects of the ability of a system to perform. Designing for reliability becomes essential to ensure that engineering systems are capable of functioning at the required and specified levels of performance, and to ensure that less costs are expended to achieve these levels of performance. Several techniques for determining reliability are categorised under three distinct definitions, namely reliability prediction, reliability assessment and reliability evaluation, according to their applicability in determining the integrity of engineering design at the conceptual, preliminary or schematic, and detail design stages respectfully. Techniques for reliability prediction are more appropriate during conceptual design, techniques for reliability assessment are more appropriate during preliminary or schematic design, and techniques for reliability evaluation are more appropriate during detail design. This chapter considers various techniques in determining reliability in engineering design at the various design stages, through the formulation of conceptual and mathematical models of engineering design integrity in designing for reliability, and the development of computer methodology whereby the models can be used for engineering design review procedures.

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References

  • Abernethy RB (1992) New methods for Weibull and log normal analysis. ASME Pap no 92-WA/DE-14, ASME, New York

    Google Scholar 

  • Agarwala AS (1990) Shortcomings in MIL-STD-1629A: guidelines for criticality analysis. In: Reliability Maintainability Symp, pp 494–496

    Google Scholar 

  • AMCP 706-196 (1976) Engineering design handbook: development guide for reliability. Part II. Design for reliability. Army Material Command, Dept of the Army, Washington, DC

    Google Scholar 

  • Andrews JD, Moss TR (1993) Reliability and risk assessment. American Society of Mechanical Engineers

    Google Scholar 

  • Artale A, Franconi E (1998) A temporal description logic for reasoning about actions and plans. J Artificial Intelligence Res JAIR, pp 463–506

    Google Scholar 

  • Ascher W (1978) Forecasting: an appraisal for policymakers and planners. John Hopkins University Press, Baltimore, MD

    Google Scholar 

  • Aslaksen E, Belcher R (1992) Systems engineering. Prentice Hall of Australia

    Google Scholar 

  • Barnett V (1973) Comparative statistical inference. Wiley, New York

    MATH  Google Scholar 

  • Barringer PH (1993) Reliability engineering principles. Barringer, Humble, TX

    Google Scholar 

  • Barringer PH (1994) Management overview: reliability engineering principles. Barringer, Humble, TX

    Google Scholar 

  • Barringer PH, Weber DP (1995) Data for making reliability improvements. Hydrocarbons Processing Magazine, 4th Int Reliability Conf, Houston, TX

    Google Scholar 

  • Batill SM, Renaud JE, Xiaoyu Gu (2000) Modeling and simulation uncertainty in multidisciplinary design optimization. In: 8th AIAA/NASA/USAF/ISSMO Symp Multidisciplinary Analysis and Optimisation, AIAA, Long Beach, CA, AIAA-200-4803, pp 5–8

    Google Scholar 

  • Bement TR, Booker JM, Sellers KF, Singpurwalla ND (2000a) Membership functions and probability measures of fuzzy sets. Los Alamos Nat Lab Rep LA-UR-00-3660

    Google Scholar 

  • Bement TR, Booker JM, Keller-McNulty S, Singpurwalla ND (2000b) Testing the untestable: reliability in the 21st century. Los Alamos Nat Lab Rep LA-UR-00-1766

    Google Scholar 

  • Bennett BM, Hoffman DD, Murthy P (1992) Lebesgue order on probabilities and some applications to perception. J Math Psychol

    Google Scholar 

  • Bezdek JC (1993) Fuzzy models—what are they and why? IEEE Transactions Fuzzy Systems vol 1, no 1

    Google Scholar 

  • Blanchard BS, Fabrycky WJ (1990) Systems engineering and analysis. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Boettner DD, Ward AC (1992) Design compilers and the labeled interval calculus. In: Tong C, Sriram D (eds) Design representation and models of routine design. Artificial Intelligence in Engineering Design vol 1. Academic Press, San Diego, CA, pp 135–192

    Google Scholar 

  • Booker JM, Meyer MA (1988) Sources and effects of inter-expert correlation: an empirical study. IEEE Trans Systems Man Cybernetics 8(1):135–142

    Article  Google Scholar 

  • Booker JM, Smith RE, Bement TR, Parkinson WJ, Meyer MA (1999) Example of using fuzzy control system methods in statistics. Los Alamos Natl Lab Rep LA-UR-99-1712

    Google Scholar 

  • Booker JM, Bement TR, Meyer MA, Kerscher WJ (2000) PREDICT: a new approach to product development and lifetime assessment using information integration technology. Los Alamos Natl Lab Rep LA-UR-00-4737

    Google Scholar 

  • Bowles JB, Bonnell RD (1994) Failure mode effects and criticality analysis. In: Annual Reliability and Maintainability Symp, pp 1–34

    Google Scholar 

  • Brännback M (1997) Strategic thinking and active decision support systems. J Decision Systems 6:9–22

    Google Scholar 

  • BS5760 (1991) Guide to failure modes, effects and criticality analysis (FMEA and FMECA). British Standard BS5760 Part 5

    Google Scholar 

  • Buchanan BG, Shortliffe EH (1984) Rule-based expert systems. Addison-Wesley, Reading, MA

    Google Scholar 

  • Buckley J, Siler W (1987) Fuzzy operators for possibility interval sets. Fuzzy Sets Systems 22:215–227

    Article  MathSciNet  Google Scholar 

  • Bull DR, Burrows CR, Crowther WJ, Edge KA, Atkinson RM, Hawkins PG, Woollons DJ (1995a) Failure modes and effects analysis. Engineering and Physical Sciences Research Council GR/J58251 and GR/J88155

    Google Scholar 

  • Bull DR, Burrows CR, Crowther WJ, Edge KA, Atkinson RM, Hawkins PG, Woollons DJ (1995b) Approaches to automated FMEA of hydraulic systems. In: Proc ImechE Congr Aerotech 95 Seminar, Birmingham, Pap C505/9/099

    Google Scholar 

  • Carlsson C, Walden P (1995a) Active DSS and hyperknowledge: creating strategic visions. In: Proc EUFIT’95 Conf, Aachen, Germany, August, pp 1216–1222

    Google Scholar 

  • Carlsson C, Walden P (1995b) On fuzzy hyperknowledge support systems. In: Proc 2nd Int Worksh Next Generation Information Technologies and Systems, Naharia, Israel, June, pp 106–115

    Google Scholar 

  • Carlsson C, Walden P (1995c) Re-engineering strategic management with a hyperknowledge support system. In: Christiansen JK, Mouritsen J, Neergaard P, Jepsen BH (eds) Proc 13th Nordic Conf Business Studies, Denmark, vol II, pp 423–437

    Google Scholar 

  • Carter ADS (1986) Mechanical reliability. Macmillan Press, London

    Google Scholar 

  • Carter ADS (1997) Mechanical reliability and design. Macmillan Press, London

    Google Scholar 

  • Cayrac D, Dubois D, Haziza M, Prade H (1994) Possibility theory in fault mode effects analyses—a satellite fault diagnosis application. In: Proc 3rd IEEE Int Conf Fuzzy Systems FUZZ-IEEE ’94, Orlando, FL, June, pp 1176–1181

    Google Scholar 

  • Cayrac D, Dubois D, Prade H (1995) Practical model-based diagnosis with qualitative possibilistic uncertainty. In: Besnard P, Hanks S (eds) Proc 11th Conf Uncertainty in Artificial Intelligence, pp 68–76

    Google Scholar 

  • Cayrol M, Farency H, Prade H (1982) Fuzzy pattern matching. Kybernetes, pp 103–106

    Google Scholar 

  • Chiueh T (1992) Optimization of fuzzy logic inference architecture. Computer, May, pp 67–71

    Google Scholar 

  • Coghill GM, Chantler MJ (1999a) Constructive and non-constructive asynchronous qualitative simulation. In: Proc Int Worksh Qualitative Reasoning, Scotland

    Google Scholar 

  • Coghill GM, Shen Q, Chantler MJ, Leitch RR (1999b) Towards the use of multiple models for diagnoses of dynamic systems. In: Proc Int Worksh Principles of Diagnosis, Scotland

    Google Scholar 

  • Conlon JC, Lilius WA (1982) Test and evaluation of system reliability, availability and maintainability. Office of the Under Secretary of Defense for Research and Engineering, DoD 3235.1-H

    Google Scholar 

  • Cox DR (1972) Regression models and life tables (with discussion). J R Stat Soc B 34:187–220

    MATH  Google Scholar 

  • Davis E (1987) Constraint propagation with interval labels. Artificial Intelligence 32:281–331

    Article  MATH  MathSciNet  Google Scholar 

  • de Kleer J, Brown JS (1984) A qualitative physics based on confluences. Artificial Intelligence 24:7–83

    Article  Google Scholar 

  • Dhillon BS (1983) Reliability engineering in systems design and operation. Van Nostrand Reinhold, Berkshire

    Google Scholar 

  • Dhillon BS (1999a) Design reliability: fundamentals and applications. CRC Press, LLC 2000, NW Florida

    Google Scholar 

  • Dubois D, Prade H (1988) Possibility theory—an approach to computerized processing of uncertainty. Plenum Press, New York

    MATH  Google Scholar 

  • Dubois D, Prade H (1990) Modelling uncertain and vague knowledge in possibility and evidence theories. Uncertainty in Artificial Intelligence vol 4. Elsevier, Amsterdam, pp 303–318

    Google Scholar 

  • Dubois D, Prade H (1992a) Upper and lower images of a fuzzy set induced by a fuzzy relation: applications to fuzzy inference and diagnosis. Information Sci 64:203–232

    Article  MATH  MathSciNet  Google Scholar 

  • Dubois D, Prade H (1992b) Fuzzy rules in knowledge-based systems modeling gradedness, uncertainty and preference. In: Zadeh LA (ed) An introduction to fuzzy logic applications in intelligent systems. Kluwer, Dordrecht, pp 45–68

    Google Scholar 

  • Dubois D, Prade H (1992c) Gradual inference rules in approximate reasoning. Information Sci 61:103–122

    Article  MATH  MathSciNet  Google Scholar 

  • Dubois D, Prade H (1992d) When upper probabilities are possibility measures. Fuzzy Sets Systems 49:65–74

    Article  MATH  MathSciNet  Google Scholar 

  • Dubois D, Prade H (1993a) Fuzzy sets and probability: misunderstandings, bridges and gaps. Report (translated), Institut de Recherche en Informatique de Toulouse (I.R.I.T.) Université Paul Sabatier, Toulouse

    Google Scholar 

  • Dubois D, Prade H (1993b) A fuzzy relation-based extension of Reggia’s relational model for diagnosis. In: Heckerman, Mamdani (eds) Proc 9th Conf Uncertainty in Artificial Intelligence, WA, pp 106–113

    Google Scholar 

  • Dubois D, Prade H, Yager RR (1993) Readings in fuzzy sets and intelligent systems. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  • Dubois D, Lang J, Prade H (1994) Automated reasoning using possibilistic logic: semantics, belief revision and variable certainty weights. IEEE Trans Knowledge Data Eng 6:64–69

    Article  Google Scholar 

  • EPRI (1974) A review of equipment aging theory and technology. Nuclear Safety & Analysis Department, Nuclear Power Division, Electricity Power Research Institute, Palo Alto, CA

    Google Scholar 

  • Fishburn P (1986) The axioms of subjective probability. Stat Sci 1(3):335–358

    Article  MathSciNet  Google Scholar 

  • Fullér R (1999) On fuzzy reasoning schemes. In: Carlsson C (ed) The State of the Art of Information Systems in 2007. Turku Centre for Computer Science, Abo, TUCS Gen Publ no 16, pp 85–112

    Google Scholar 

  • Grant Ireson W, Coombs CF, Moss RY (1996) Handbook of reliability engineering and management. McGraw-Hill, New York

    Google Scholar 

  • ICS (2000) The RAMS plant analysis model. ICS Industrial Consulting Services, Gold Coast City, Queensland

    Google Scholar 

  • IEEE Std 323-1974 (1974) IEEE Standard for Qualifying Class IE Equipment for Nuclear Power Generating Stations. Institute of Electrical and Electronics Engineers, New York

    Google Scholar 

  • Kerscher W, Booker J, Bement T, Meyer M (1998) Characterizing reliability in a product/process design-assurance program. In: Proc Int Symp Product Quality and Integrity, Anaheim, CA, and Los Alamos Lab Rep LA-UR-97-36

    Google Scholar 

  • Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic theory and application. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Kuipers B (1990) Qualitative simulation. Artificial Intelligence 29(3):289–338 (1986), reprinted in Qualitative reasoning about physical systems, Morgan Kaufman, San Mateo, CA, pp 236–260

    MathSciNet  Google Scholar 

  • Laviolette M, Seaman J Jr, Barrett J, Woodall W (1995) A probabilistic and statistical view of fuzzy methods. Technometrics 37:249–281

    Article  MATH  Google Scholar 

  • Lee RCT (1972) Fuzzy logic and the resolution principle. J Assoc Computing Machinery 19:109–119

    MATH  Google Scholar 

  • Liu JS, Thompson G (1996) The multi-factor design evaluation of antenna structures by parameter profile analysis. Proc Inst Mech Engrs Part B, J Eng Manufacture 210:449–456

    Article  Google Scholar 

  • Loginov VI (1966) Probability treatment of Zadeh membership functions and their use in pattern recognition. Eng Cybernetics 68–69

    Google Scholar 

  • Martz HF, Almond RG (1997) Using higher-level failure data in fault tree quantification. Reliability Eng System Safety 56(1):29–42

    Article  Google Scholar 

  • Mavrovouniotis M, Stephanopoulos G (1988) Formal order of magnitude reasoning in process engineering. Computers Chem Eng 12:867–881

    Article  Google Scholar 

  • Meyer MA, Booker JM (1991) Eliciting and analyzing expert judgment: a practical guide. Academic Press, London

    Google Scholar 

  • Meyer MA, Butterfield KB, Murray WS, Smith RE, Booker JM (2000) Guidelines for eliciting expert judgement as probabilities or fuzzy logic. Los Alamos Natl Lab Rep LA-UR-00-218

    Google Scholar 

  • MIL-STD-721B (1980) Definition of terms for reliability and maintainability. Department of Defense (DoD), Washington, DC

    Google Scholar 

  • MIL-STD-1629 (1980) Procedures for performing a failure mode, effects, and criticality analysis. DoD, Washington, DC

    Google Scholar 

  • Moore R (1979) Methods and applications of interval analysis. SIAM, Philadelphia, PA

    MATH  Google Scholar 

  • Moss TR, Andrews JD (1996) Reliability assessment of mechanical systems. Proc Inst Mech Engrs vol 210

    Google Scholar 

  • Natvig B (1983) Possibility versus probability. Fuzzy Sets Systems 10:31–36

    Article  MATH  MathSciNet  Google Scholar 

  • Norwich AM, Turksen IB (1983) A model for the measurement of membership and the consequences of its empirical implementation. Fuzzy Sets Systems 12:1–25

    Article  MathSciNet  Google Scholar 

  • Orchard RA (1998) FuzzyCLIPS Version 6.04A. Integrated Reasoning, Institute for Information Technology, National Research Council Canada

    Google Scholar 

  • Ortiz NR, Wheeler TA, Breeding RJ, Hora S, Meyer MA, Keeney RL (1991) The use of expert judgment in NUREG-1150. Nuclear Eng Design 126:313–331 (revised from Sandia Natl Lab Rep SAND88-2253C, and Nuclear Regulatory Commission Rep NUREG/CP-0097 5, pp 1–25

    Google Scholar 

  • Pahl G, Beitz W (1996) Engineering design. Springer, Berlin Heidelberg New York

    Google Scholar 

  • Payne S (1951) The art of asking questions. Princeton University Press, Princeton, NJ

    Google Scholar 

  • Raiman O (1986) Order of magnitude reasoning. In: Proc 5th National Conf Artificial Intelligence AAAI-86, pp 100–104

    Google Scholar 

  • ReliaSoft Corporation (1997) Life data analysis reference. ReliaSoft Publ, Tucson, AZ

    Google Scholar 

  • Roberts FS (1979) Measurement theory. Addison-Wesley, Reading, MA

    MATH  Google Scholar 

  • Ryan M, Power J (1994) Using fuzzy logic—towards intelligent systems. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Shen Q, Leitch R (1993) Fuzzy qualitative simulation. IEEE Trans Systems Man Cybernetics 23(4), and J Math Anal Appl 64(2):369–380 (1993)

    Google Scholar 

  • Shortliffe EH (1976) Computer-based medical consultation: MYCIN. Elsevier, New York

    Google Scholar 

  • Simon HA (1981) The sciences of the artificial. MIT Press, Cambridge, MA

    Google Scholar 

  • Smith RE, Booker JM, Bement TR, Meyer MA, Parkinson WJ, Jamshidi M (1998) The use of fuzzy control system methods for characterizing expert judgment uncertainty distributions. In: Proc PSAM 4 Int Conf, September, pp 497–502

    Google Scholar 

  • Sosnowski ZA (1990) FLISP—a language for processing fuzzy data. Fuzzy Sets Systems 37:23–32

    Article  MATH  Google Scholar 

  • Steele AD, Leitch RR (1996) A strategy for qualitative model-based diagnosis. In: Proc IFAC-96 13th World Congr, San Francisco, CA, vol N, pp 109–114

    Google Scholar 

  • Steele AD, Leitch RR (1997) Qualitative parameter identification. In: Proc QR-97 11th Int Worksh Qualitative Reasoning About Physical Systems, pp 181–192

    Google Scholar 

  • Thompson G, Geominne J, Williams JR (1998) A method of plant design evaluation featuring maintainability and reliability. Proc Inst Mech Engrs vol 212 Part E

    Google Scholar 

  • Thompson G, Liu JS, Hollaway L (1999) An approach to design for reliability. Proc Inst Mech Engrs vol 213 Part E

    Google Scholar 

  • Walden P, Carlsson C (1995) Hyperknowledge and expert systems: a case study of knowledge formation processes. In: Nunamaker JF (ed) Information systems: decision support systems and knowledge-based systems. Proc 28th Annu Hawaii Int Conf System Sciences, IEEE Computer Society Press, Los Alamitos, CA, vol III, pp 73–82

    Google Scholar 

  • Whalen T, Schott B (1983) Issues in fuzzy production systems. Int J Man-Machine Studies 19:57

    Article  Google Scholar 

  • Whalen T, Schott B, Ganoe F (1982) Fault diagnosis in fuzzy network. Proc 1982 Int Conf Cybernetics and Society, IEEE Press, New York

    Google Scholar 

  • Wirth R, Berthold B, Krämer A, Peter G (1996) Knowledge-based support of system analysis for failure mode and effects analysis. Eng Appl Artificial Intelligence 9(3):219–229

    Google Scholar 

  • Wolfram J (1993) Safety and risk: models and reality. Proc Inst Mech Engrs vol 207

    Google Scholar 

  • Yen J, Langari R, Zadeh LA (1995) Industrial applications of fuzzy logic and intelligent systems. IEEE Press, New York

    MATH  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Information Control 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

  • Zadeh LA (1968) Probability measures of fuzzy events. J Math Anal Appl 23:421–427

    Article  MATH  MathSciNet  Google Scholar 

  • Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Systems Man Cybernetics 2:28–44

    MathSciNet  Google Scholar 

  • Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning I–III. Elsevier, New York, Information Sci 8:199–249, 9:43–80

    Google Scholar 

  • Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Systems 1:3–28

    Article  MATH  MathSciNet  Google Scholar 

  • Zadeh LA (1979) A theory of approximate reasoning. In: Hayes J, Michie D, Mikulich LI (eds) Machine Intelligence, vol 9. Wiley, New York, pp 149–194

    Google Scholar 

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(2009). Reliability and Performance in Engineering Design. In: Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design. Springer, London. https://doi.org/10.1007/978-1-84800-175-6_3

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