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.

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, 2013
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3. Predicting Reliability in
Product‘s Design Phase
Rong Pan, Ph.D.
Associate Professor
Arizona State University
This speaker is currently on his sabbatical in the National University of
Singapore.
This talk is based on the paper, ―An Enhanced Parenting Process:
Predicting Reliability in Product's Design Phase‖ by Luis Mejia and
Rong Pan, published on Quality Engineering in 2011.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Expert Opinion Survey

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. Multiple Experts
 Combining multiple experts‘ opinions
 To determine weights
◦ All equal weights
◦ Proportional to a ranking system
◦ Self weights
◦ Calibration

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. 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. Analyzing Parents

20. Expert Inputs

21. Parenting Factor

22. Failure Rates of New Design

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. 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.