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8.   Assessing Product Reliability - Detailed Table of Contents  [8.]



  1. Introduction  [8.1.]
    1. Why is the assessment and control of product reliability important?  [8.1.1.]
      1. Quality versus reliability  [8.1.1.1.]
      2. Competitive driving factors  [8.1.1.2.]
      3. Safety and health considerations  [8.1.1.3.]
    2. What are the basic terms and models used for reliability evaluation?  [8.1.2.]
      1. Repairable systems, non-repairable populations and lifetime distribution models  [8.1.2.1.]
      2. Reliability or survival function  [8.1.2.2.]
      3. Failure (or hazard) rate  [8.1.2.3.]
      4. "Bathtub" curve  [8.1.2.4.]
      5. Repair rate or ROCOF  [8.1.2.5.]
    3. What are some common difficulties with reliability data and how are they overcome?  [8.1.3.]
      1. Censoring  [8.1.3.1.]
      2. Lack of failures  [8.1.3.2.]
    4. What is "physical acceleration" and how do we model it?  [8.1.4.]
    5. What are some common acceleration models?  [8.1.5.]
      1. Arrhenius  [8.1.5.1.]
      2. Eyring  [8.1.5.2.]
      3. Other models  [8.1.5.3.]
    6. What are the basic lifetime distribution models used for non-repairable populations?  [8.1.6.]
      1. Exponential  [8.1.6.1.]
      2. Weibull  [8.1.6.2.]
      3. Extreme value distributions  [8.1.6.3.]
      4. Lognormal  [8.1.6.4.]
      5. Gamma  [8.1.6.5.]
      6. Fatigue life (Birnbaum-Saunders)  [8.1.6.6.]
      7. Proportional hazards model  [8.1.6.7.]
    7. What are some basic repair rate models used for repairable systems?  [8.1.7.]
      1. Homogeneous Poisson Process (HPP)  [8.1.7.1.]
      2. Non-Homogeneous Poisson Process (NHPP) - power law  [8.1.7.2.]
      3. Exponential law  [8.1.7.3.]
    8. How can you evaluate reliability from the "bottom-up" (component failure mode to system failure rate)?  [8.1.8.]
      1. Competing risk model  [8.1.8.1.]
      2. Series model  [8.1.8.2.]
      3. Parallel or redundant model  [8.1.8.3.]
      4. R out of N model  [8.1.8.4.]
      5. Standby model  [8.1.8.5.]
      6. Complex systems  [8.1.8.6.]
    9. How can you model reliability growth?  [8.1.9.]
      1. NHPP power law  [8.1.9.1.]
      2. Duane plots  [8.1.9.2.]
      3. NHPP exponential law  [8.1.9.3.]
    10. How can Bayesian methodology be used for reliability evaluation?  [8.1.10.]

  2. Assumptions/Prerequisites  [8.2.]
    1. How do you choose an appropriate life distribution model?  [8.2.1.]
      1. Based on failure mode  [8.2.1.1.]
      2. Extreme value argument  [8.2.1.2.]
      3. Multiplicative degradation argument  [8.2.1.3.]
      4. Fatigue life (Birnbaum-Saunders) model  [8.2.1.4.]
      5. Empirical model fitting - distribution free (Kaplan-Meier) approach  [8.2.1.5.]
    2. How do you plot reliability data?  [8.2.2.]
      1. Probability plotting  [8.2.2.1.]
      2. Hazard and cum hazard plotting  [8.2.2.2.]
      3. Trend and growth plotting (Duane plots)  [8.2.2.3.]
    3. How can you test reliability model assumptions?  [8.2.3.]
      1. Visual tests  [8.2.3.1.]
      2. Goodness of fit tests  [8.2.3.2.]
      3. Likelihood ratio tests  [8.2.3.3.]
      4. Trend tests  [8.2.3.4.]
    4. How do you choose an appropriate physical acceleration model?  [8.2.4.]
    5. What models and assumptions are typically made when Bayesian methods are used for reliability evaluation?  [8.2.5.]

  3. Reliability Data Collection  [8.3.]
    1. How do you plan a reliability assessment test?  [8.3.1.]
      1. Exponential life distribution (or HPP model) tests  [8.3.1.1.]
      2. Lognormal or Weibull tests  [8.3.1.2.]
      3. Reliability growth (Duane model)  [8.3.1.3.]
      4. Accelerated life tests  [8.3.1.4.]
      5. Bayesian gamma prior model  [8.3.1.5.]

  4. Reliability Data Analysis  [8.4.]
    1. How do you estimate life distribution parameters from censored data?  [8.4.1.]
      1. Graphical estimation  [8.4.1.1.]
      2. Maximum likelihood estimation  [8.4.1.2.]
      3. A Weibull maximum likelihood estimation example  [8.4.1.3.]
    2. How do you fit an acceleration model?  [8.4.2.]
      1. Graphical estimation  [8.4.2.1.]
      2. Maximum likelihood  [8.4.2.2.]
      3. Fitting models using degradation data instead of failures  [8.4.2.3.]
    3. How do you project reliability at use conditions?  [8.4.3.]
    4. How do you compare reliability between two or more populations?  [8.4.4.]
    5. How do you fit system repair rate models?  [8.4.5.]
      1. Constant repair rate (HPP/exponential) model  [8.4.5.1.]
      2. Power law (Duane) model  [8.4.5.2.]
      3. Exponential law model  [8.4.5.3.]
    6. How do you estimate reliability using the Bayesian gamma prior model?  [8.4.6.]
    7. References For Chapter 8: Assessing Product Reliability  [8.4.7.]
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