reliability n.
Skip this Video
Loading SlideShow in 5 Seconds..
Reliability PowerPoint Presentation
Download Presentation

Loading in 2 Seconds...

play fullscreen
1 / 43

Reliability - PowerPoint PPT Presentation

  • Uploaded on

Reliability. Extending the Quality Concept. ASQ CQA CQE CSSBB CRE APICS CPIM. Director of Product Integrity & Reliability for Stoneridge TED Background in metallurgy & materials science. Kim Pries. What is reliability? Reliability data Probability distributions

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Reliability' - mollie-duran

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript


Extending the Quality Concept

kim pries







Director of Product Integrity & Reliability for Stoneridge TED

Background in metallurgy & materials science

Kim Pries
summary slide
What is reliability?

Reliability data

Probability distributions

Most common distribution

Weibull mean


Shapes of Weibull

Scale of Weibull

Location of Weibull

Gamma distribution

Non-parametric data fit

Summary Slide
what is reliability
What is reliability?
  • Reliability is the “quality concept” applied over time
  • Reliability engineering requires a different tool box
reliability data
Reliability data
  • Nearly always “units X to failure,” where units are most often
    • Miles
    • Hours (days, weeks, months)
probability distributions
Probability distributions
  • Exponential
    • “Random failure”
  • Log-normal
  • Weibull
  • Gamma
most common distribution
Most common distribution


  • Weibull distribution

eta = scale parameter,

beta = shape parameter (or slope),

gamma = location parameter.

weibull mean
Weibull mean
  • Also known as MTBF or MTTF
  • Need to understand gamma function
  • Using diagrams from Reliasoft Weibull++ 7.x
  • A few from Minitab
summary slide1
Accelerated life testing

Accelerated Life Testing

Highly accelerated life testing

Multi-environment overstress

MEOST, continued



Achieving reliability growth

Reliability Growth-Duane Model

Reliability Growth-AMSAA model

Summary Slide
accelerated life testing1
Accelerated Life Testing
  • Can be used to predict life based on testing
  • A typical model looks like
highly accelerated life testing
Highly accelerated life testing
  • No predictive value
  • Reveals weakest portions of design
  • Examples:
    • Thermal shock
    • Special drop testing
    • Mechanical shock
    • Swept sine vibration
multi environment overstress
Derate components

Study thermal behavior


Finite element analysis

Modular designs


Mfg line ‘escapes’


Robust…high S/N ratio

Design for maintainability

Product liability analysis

Take apart supplier products


Multi-environment overstress
meost continued
MEOST, continued
  • Test to failure is goal
  • Combined stress environment
  • Beyond design levels
  • Lower than immediate destruct level
  • Example:
    • Simultaneous
      • Temperature
      • Humidity
      • Vibration
step stress
  • Cumulative damage model
  • Harder to relate to reality
hass and hasa
  • Screening versus sampling
  • Small % of life to product
  • Elicit ‘infant mortality’ failures
  • Example:
    • Burn-in
achieving reliability growth
Achieving reliability growth
  • Detect failure causes
  • Feedback
  • Redesign
  • Improved fabrication
  • Verification of redesign
reliability growth amsaa model
Cumulative failures

Initially very poor

Improves over time

Reliability Growth-AMSAA model
summary slide2
Effects of design

Effects of manufacturing

Can’t we predict?



Serial reliability

Parallel reliability (redundancy)

Other tools

Software reliability

Summary Slide
effects of design
Effects of design
  • Usually the heart of warranty issues
  • Counteract with robust design
effects of manufacturing
Effects of manufacturing
  • Manufacturing can degrade reliability
  • Cannot improve intrinsic design issues
can t we predict
Can’t we predict?
  • MIL-HDBK-217F
    • No parallel circuits
    • Electronics only
    • Extremely conservative
      • Leads to over-engineering
      • Excessive derating
      • Off by factors of at least 2 to 4
  • 1-dimensional
    • Example: miles only
  • 2-dimensional
    • Example:
      • Miles
      • Years
  • Non-renewing
  • Pro-rated
  • Cumulative
    • Multiple items
  • Reliability improvement
serial reliability
Serial reliability
  • Simple product of the probabilities of failure of components
  • More components = less reliability
parallel reliability redundancy
Parallel reliability (redundancy)
  • Dramatically reduces probability of failure
other tools
Other tools
  • FMEA
  • Fault Tree Analysis
  • Reliability Block Diagrams
    • Simulation
software reliability
Software reliability
  • Difficult to prove
  • Super methods
    • B-method
    • ITU Z.100, Z.105, and Z.120
    • Clean room
summary slide3
Summary Slide
  • What about maintenance?
  • Pogo Pins
  • Pogo Pins (product 1)
  • Pogo Pins (Product 2)
  • Pogo Pin conclusions
  • Preventive vs. Predictive
what about maintenance
What about maintenance?
  • Same math
  • Looking for types of wear and other failure modes
pogo pin conclusions
Pogo Pin conclusions
  • Very quick “infant mortality”
  • Random failure thereafter
  • Difficult to find a nice preventive maintenance schedule
  • Frequent inspection
preventive vs predictive
Preventive vs. Predictive
  • Preventive maintenance
    • Fix before it breaks
    • Statistically based intervals
  • Predictive maintenance
    • Detect anomalies
    • Always uses sensors
the future
The future
  • Combinatorial testing
    • Designed experiments
      • Response surfaces
      • Analysis of variance
      • Analysis of covariance
  • Eyring models
    • Multiple environments