Reliability Maintenance Engineering 1 - 5 Measuring Reliability

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Reliability Maintenance Engineering Day 1 session 5 Measuring Reliability
Three day live course focused on reliability engineering for maintenance programs. Introductory material and discussion ranging from basic tools and techniques for data analysis to considerations when building or improving a program.

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  • Bathtub curve
  • Balance between investment and value
  • PDF Plots
  • The formula for the probability density function of the general Weibull distribution iswhere is the shape parameter, is the location parameter and is the scale parameter. The case where = 0 and = 1 is called the standard Weibull distribution. The case where = 0 is called the 2-parameter Weibull distribution. The equation for the standard Weibull distribution reduces to
  • Balance between investment and value
  • Repair data analysis
  • Cdf with some subsystems underneath
  • Balance between investment and value
  • Cdf with some subsystems underneath
  • Balance between investment and value
  • Reliability Maintenance Engineering 1 - 5 Measuring Reliability

    1. 1. Reliability Engineering Fred Schenkelberg fms@fmsreliability.com
    2. 2. MEASURING RELIABILITY TO IMPROVE SAFETY AND AVAILABILITY Day 1 Session 5
    3. 3. Objectives • Reliability and the bathtub curve • Calculating PDF plot • What to do if you don’t have data at failure mode level • Measuring reliability of Line Replaceable Units • Developing the next generation reliability growth analysis
    4. 4. Early Life Failures Decreasing hazard rate Typical failure causes • Manufacturing defect • Shipping damage • Installation damage
    5. 5. Useful Life Constant hazard rate Typical failure causes • Overstress • Random events Note: rarely occurs in real life – may be useful if change in hazard rate is small enough
    6. 6. Wear out Increasing hazard rate Typical failure causes • Material wear • Rust/Oxidation • Creep/Crack growth • Embrittlement
    7. 7. Discussion & Questions
    8. 8. Probability Distribution Plot • Like a histogram • Construction notes • Continuous distributions • Discrete distributions f (x)dx = Pr a £ X £ b[ ]a b ò f x( )= Pr X = x[ ]
    9. 9. How to use PDF plot • Probability plots • How many will survive • How many will fail
    10. 10. Discussion & Questions
    11. 11. Data and Level • Failure mechanism level • Subunit level (LRU) • Unit Level • System level
    12. 12. Plots and levels • CDF with system and subunit information • Continuous Distributions • Discrete Distributions F x( )= f m( )dm -¥ ¥ ò F x( )= f i( ) i=0 x å
    13. 13. time ProbabilityofFailure(logscale)
    14. 14. Repairable Equipment • MCF example • How to interpret – Concave – Convex – Straight Time CumulativeFailures
    15. 15. Discussion & Questions
    16. 16. Reliability Growth • CDF reflecting changes • Look for change in slope • Look for change in scale
    17. 17. time ProbabilityofFailure(logscale) or
    18. 18. Cumulative Failures over time • During development or improvement projects • Plot total failures over time (more later with Duane and related plots)
    19. 19. Discussion & Questions
    20. 20. Summary • Reliability and the bathtub curve • Calculating PDF plot • What to do if you don’t have data at failure mode level • Measuring reliability of Line Replaceable Units • Developing the next generation reliability growth analysis Measuring Reliability to improve Safety and Availability

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