3 Rel Models
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RelexReliab i l ity So ftwarethe intuitive solution!
Relex Softw are Corporat ion
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What is Relex?
A Powerful Reliability Software Tool
performs efficient reliability analysis uses multiple analysis techniques
provides advanced features
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Relex Is Uniquely Qual i f ied
Reliability Engineering Experience
Commercial
Military
Software Development Experience
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RelexReliab i l ity So ftwarethe intuitive solution!
Relex Softw are Corporat ion
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Introduction toReliability Prediction
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Reliability Predictions
What is a Rel iab i l i ty Predic t ion?
Calculation of failure rate (MTBF)
How is it Calcu lated?
Based on established reliability model
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Reliability Measures
Failure Rate ()
Mean Time Between Failures (MTBF)
Reliability
Availability
Samp le Relex Reliabil i ty
Predict io n calculat ion results
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Failure Rate
Defined As:
Rate of Occurrence of Failures
Number of Failure in Specified
Time Period
Units:
Failures per Million Hours
Failures per Billion Hours (FIT Rate)
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MTBF
Defined As:
Mean Time Between Failures
Number of Hours to Pass
Before a Failure Occurs
Inverse of Failure Rate*
Units:
Typically expressed in Hours
*Constant Failure Rate Systems
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Reliability
Defined As:
The probability that an item will perform a
required function without failure under
stated conditions for a stated period of
time
Units:
Probability Value (0-1)
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Availability
Defined As:
The probability that an item is in an
operable state at any time
Units: Probability Value (0-1)
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Reliability Summary
Failure Rate -- number of failures in time
MTBF -- average time between failures
Reliability -- takes into account mission time
Availability -- accounts for repairs (MTTR)and downtime
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The Bathtub Curve
and Reliability
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The Bathtub Curve
Represents failure rate tendencies for
the lifespan of an item Failure rate varies in different phases of
life
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Three Phases of Life
Infant Mortality Region
Wear-Out Region
Constant Failure Rate Region
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Bathtub Curve
Graph of Failure Rate vs. Time
Considers three phases of life
Represents lifespan of item
(i.e. 15 years for a car)
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Time
FailureRa
te
Infant Mortality
Wear Out
Constant Failure Rate
Bathtub Curve
Illustration
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Reliability Models
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Influences to reliability /
Model-parameters
Production
maturityStorage
conditions
Transportconditions
Design &
construction
Material-selection
Application-
temperature
mechanical
stressClimatic
environment
electrical
stress
Operating
conditions
Electronic
component
Productionfactors
Applicationfa
ctors
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Relex Prediction Models
MIL-HDBK-217 (FN1, FN2 )
Telcordia (Telcordia 1, Bellcore 4,5,6)
Prism: RAC model (Process Grades, Bayesian)
NSWC-98/LE1: mechanical model HRD5: British telecomm model
CNET 93: French telecomm model
299B: Chinese standard
Relex allows the user to use multiple models within one project and
use functionality across models (i.e. use Prism process grade factors
on 217 predicted failure rates, use Bellcore methods on 217
calculations, etc.)
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MIL-HDBK-217
Original standard for reliability
Reliability math models electronic devices
Used commercially & in the defense industry
Currently at Revision F Notice 2
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Parts Count
A section of MIL-HDBK-217
Provides simpler reliability math
Typical Uses:
Used early in the design process
Used to acquire a rough estimate of reliability
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Telcordia (Bellcore)
Originally developed at AT&T Bell Labs
Modified MIL-HDBK-217 equations New equations represented what their
equipment was experiencing in the field
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Telcordia (Bellcore) (cont.)
New model with new feature
Account for real data Burn-in, Field, Laboratory testing data
Popular standard for commercial
companies
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Mechanical
Based on the Handbook of Reliability
Prediction Procedures for Mechanical
Equipment, NSWC-98/LE1
Provides models for various types of
mechanical devices including springs,
bearings, seals, etc.
New and unique standard
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CNET & HRD5
Used in Europe
Reliability models for telecommunications
Current Versions:
HRD - 5
CNET - 93
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Bellcore vs. 217
Recognition & Acceptance Concentration
Calculations & Equations
Consideration of Test Data
Multiplier
Parts Environments
Quality Levels
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Accuracy of MTBF
Assessments
Stage I:Parts count method, assuming
constant failure rates
Stage II:Variation of failure rates according topart families
Stage III:Taking into account of operational
parameters
Stage IV:
Consideration of failure modes,
time influences, different failure
distribution for each part, etc.
Accuracy
Time spent for the analysis
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PRISM Reliability
Model
Developed by the Reliability Analysis Center (RAC)
Accounts for the effect of process related variability
on system failure rate
Inherent failure rate based on base failure rate and
environmental conditions (RAC Rates model)
Failure rate may then be modified by:
Process Grade Factors, and/or
Bayesian Analysis, and/or
Predecessor Data
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PRISM Methodology
RAC
Component
Models
System Reliability
AssessmentModel
RAC Failure
Rate Databases
Historical Data
on Similar
Systems
Process
Assessments
Software
Model
Test Data
Bayesian
DataCombination
System
Reliability
Estimate
Operational Profile,
Environmental and
Electrical Stresses
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Primary Causes of Failure
14
63
38
.2
4
1
Parts
22%
Manufacturing
15%
Design
9%System
Management
4%
Wearout
9%
Induced
12%
No Defect
20%
Software
9%
(Nominal Values)
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PRISM Process Grade
Factor Types
Design
Manufacturing
Parts Quality System Management
CND (Can Not Duplicate)
Induced Wearout
Growth
Infant Mortality
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Other PRISM Adjustments
Bayesian
Uses test and field data to enhance
predicted failure rate
Predecessor
Uses previous history data to further refine
predicted failure rate
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PRISM Note
Although PRISM contains RAC Rate models for
many part types, it does not include the following:
Rotating devices Relays
Switching devices Tubes
Connections Lasers
Miscellaneous parts
Relex can solve this problem by allowing the user to
apply PRISM concepts (Process Grade, Bayesian,Predecessor) to a failure rate calculated by all other
models.