Early product reliability prediction


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The philosophy of build-in reliability (BIR) or design for reliability (DFR) emphasizes the value of reliability prediction at a product’s very early design stage. Due to the lack of reliability data, the reliability prediction in this phase often utilizes auxiliary information such as the reliability information of similar products or components. In this talk, we discuss an enhanced parenting process, which consists of rigorous mathematical formulations and provides statistical inference on the failure rate of the new product. The talk is based on our paper entitled “An enhanced parenting process: predicting reliability in product’s design phase”, published on Quality Engineering in 2011.

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Early product reliability prediction

  1. 1. Early Product Reliability  Prediction  (产品可靠性早期预测) Dr. Rong Pan (潘荣博士) ©2013 ASQ & Presentation Pan Presented live on Mar 16th, 2013http://reliabilitycalendar.org/webinar p y gs/chinese/
  2. 2. ASQ Reliability Division  ASQ Reliability Division Chinese Webinar Series Chinese Webinar Series One of the monthly webinars  One of the monthly webinars on topics of interest to  reliability engineers. To view recorded webinar (available to ASQ Reliability  ( y Division members only) visit asq.org/reliability To sign up for the free and available to anyone live webinars  To sign up for the free and available to anyone live webinars visit reliabilitycalendar.org and select English Webinars to  find links to register for upcoming eventshttp://reliabilitycalendar.org/webinar p y gs/chinese/
  3. 3. Predicting Reliability inProduct‘s Design Phase Rong Pan, Ph.D. Associate Professor Arizona State UniversityThis speaker is currently on his sabbatical in the National University ofSingapore.This talk is based on the paper, ―An Enhanced Parenting Process:Predicting Reliability in Products Design Phase‖ by Luis Mejia andRong Pan, published on Quality Engineering in 2011.
  4. 4. Outline Introduction ◦ Design for reliability ◦ Reliability information ◦ Parenting process Methodology ◦ Finding parents ◦ Extracting information from parents ◦ Eliciting expert opinions on design changes ◦ Incorporating parenting with expert opinions ◦ Predicting reliability of new product Illustrative example Conclusion
  5. 5. Design for Reliability Reliability study is used to a backend process ◦ Dealing with customer complaints, returns ◦ Investigating early field failures ◦ Analyzing warranty Competitive manufacturing environment demands the shift of reliability attitude ◦ Design for reliability ◦ Build-in reliability ◦ FMECA
  6. 6. Reliability Information Lack of direct reliability data for a new product ◦ No field failure data ◦ Limited test data Multiple sources of relevant reliability information ◦ Parent products ◦ Expert opinions ◦ Component failure data ◦ System simulation ◦ Literature ◦ Information from industry, trade and competitors
  7. 7. Literature Fajdiga et al. 1996; Minehane et al. 2000 ◦ Computer-supported analysis (i.e., computer simulation) is widely used by designers and engineers ◦ The goal of reliability simulation is to help the designer to achieve the reliability requirement while minimizing the use of resources Guerin et al. (2003) ◦ Three different methods to assess the failure probability— propagation of error, Monte Carlo simulation, and first-order reliability methods ◦ Use dependability studies to define a prior distribution for reliability estimation. Braglia et al. (2007) ◦ An adaptation of quality function deployment (QFD) to the reliability environment called the House of Reliability Chin et al. (2008) ◦ A fuzzy-based, knowledge-based Failure Mode and Effect Analysis (FMEA) to incorporate customer requirements, engineering characteristics, and critical parts characteristics
  8. 8. Parenting Process A sensible approach to predict a new product‘s reliability at its very early design stage is to use reliability information from these existing products (or parents) and map design changes to reliability quantification Difference between new product and its parents ◦ New or enhanced functions ◦ New or improved materials ◦ New or redesigned components ◦ Altered system design ◦ Note that these design changes are typically not driven by reliability concerns
  9. 9. Methodology The parenting process helps to align the technical expectation of the new product‘s reliability with the realistic estimation based on its parent‘s warranty history A ‗‗parent factor‘‘ is elicited to take into account the risk releaser/aggravators as a result of design changes in the new product
  10. 10. Finding Parents Product development is an evolving process ◦ Selecting the parent (or parents) during the design phase will determine the failure structure of the new product if no new failure modes are introduced due to the design change The warranty database of parent products is the source of information for finding failure modes and failure causes ◦ Failure causes (ci): vibration, excessive loading, misassembly, etc. ◦ Failure modes (mj): material crack, distortion, leakage, etc.
  11. 11. Parent Matrix A failure structure represents the logical interrelationship from failure causes to a specific failure mode Failure structures can be obtained empirically through warranty analysis from similar products ◦ This results in the parent matrix
  12. 12. Important Indices The importance index represents the relative importance of a failure cause (ci) to a failure mode (mj) When the failure structure is unknown, Ii,j can be obtained based on the relationships of ci and mj outlined in the warranty database and engineering knowledge ◦ qij is the standardized frequency of failure cause i when failure mode j occurs
  13. 13. Elicitation Process A risk assessment would provide the necessary measures to acknowledge uncertainties created by the introduction of changes in the new product Expert elicitation is the synthesis of experts‘ knowledge on one or more uncertain quantities A questionnaire tool to facilitate the elicitation process of experts‘ opinions on the risks of new product designs
  14. 14. Expert Opinion Survey
  15. 15. Elicitation Procedure According to Cooke (1991), experts are comfortable with a two-step procedure—the assessment is divided into ‗‗best estimate‘‘ and ‗‗degree of uncertainty‘‘ tasks ◦ 1. The expert provides an estimate of the median for the parameter in question, in this case, for the median of ci, which represents the magnitude in change (i.e., for failure rate or MTTF) from the parent to the new design for the failure cause ci. ◦ 2. The expert is asked how certain he or she is about the estimates elicited providing an upper and lower limit, with confidence level of 95% that the true value lies within the interval
  16. 16. Multiple Experts Combining multiple experts‘ opinions To determine weights ◦ All equal weights ◦ Proportional to a ranking system ◦ Self weights ◦ Calibration
  17. 17. Failure Probability of New Design Occurrence rate of failure cause ◦ Assume lognormal distribution ◦ Updates Failure probability due to a cause ◦ Assume exponential failure time Occurrence rate of failure mode ◦ The parent matrix I is used to transform Fci to Fmj under the assumption that the failure mode and failure cause relationship will not be altered in the new design
  18. 18. Example A new cylinder head gasket (CHG) is being introduced for use in a diesel engine ◦ A CHG is the most critical sealing application between the cylinder block and cylinder head ◦ The new CHG maintains the same failure structure as the previous design The warranty database of old generation CHGs is analyzed ◦ Failure causes: nonstandard design (c1), fatigue (c2), unreasonable dimension (c3) ◦ Failure modes: gas leakage (m1), and water leakage (m2).
  19. 19. Analyzing Parents
  20. 20. Expert Inputs
  21. 21. Parenting Factor
  22. 22. Failure Rates of New Design
  23. 23. Conclusion Information for early reliability prediction ◦ From parents (objective) ◦ From experts (subjective) Enhanced parenting process ◦ Combine relevant information ◦ Establish a baseline to initiate reliability thinking at an early stage of product design Be aware of elicitation bias
  24. 24. References Braglia, M., Fantoni, G., Frosolini, M. (2007). The house of reliability. International Journal of Quality & Reliability Management, 24(4):420–440. Chin, K. S., Chan, A., Yang, J. B. (2008). Development of a fuzzy fmea based product design system. International Journal of Advanced Manufacturing Technology, 36(7– 8):633–649. Cooke, R. (1991). Experts in Uncertainty: Opinion and Subjective Probability in Science. New York, NY: Oxford University Press Fajdiga, M., Jurejevcic, T., Kernc, J. (1996). Reliability prediction in early phases of product design. Journal of Engineering Design, 7(2):107–128. Guerin, F., Dumon, B., Usureau, E. (2003). Reliability estimation by bayesian method: Definition of prior distribution using dependability study. Reliability Engineering & System Safety, 82(3):299–306. Minehane, S., Duane, R., O‘Sullivan, P., McCarthy, K. G., Mathewson, A. (2000). Design for reliability. Microelectronics Reliability, 40(8–10): 1285–1294.