Relex
Reliability Software
“the intuitive solution!”
Relex Software Corporation
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What is Relex?
A Powerful Reliability Software Tool…
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performs efficient reliability analysis
uses multiple analysis techniques
provides advanced features
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Relex Is Uniquely Qualified
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Reliability Engineering Experience
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Commercial
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Military
Software Development Experience
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Relex
Reliability Software
“the intuitive solution!”
Relex Software Corporation
Introduction to
Reliability Prediction
Reliability Predictions
What is a Reliability Prediction?

Calculation of failure rate (MTBF)
How is it Calculated?

Based on established reliability model
6
Reliability Measures
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Failure Rate ()
Mean Time Between Failures (MTBF)
Reliability
Availability
Sample Relex Reliability
Prediction calculation results
Failure Rate
Defined As:
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Rate of Occurrence of Failures
Number of Failure in Specified
Time Period
Units:
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Failures per Million Hours
Failures per Billion Hours (FIT Rate)
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MTBF
Defined As:
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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)
10
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
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
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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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Failure Rate
Bathtub Curve
–Illustration–
Infant Mortality
Wear Out
Constant Failure Rate
Time
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Reliability Models
Production factors
Influences to reliability /
Model-parameters
Design &
construction
Production
maturity
Storage
conditions
Transport
conditions
Materialselection
Application factors
Electronic
component
Applicationtemperature
Operating
conditions
electrical
stress
Climatic
environment
mechanical
stress
Relex Prediction Models
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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.)
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
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Provides simpler reliability math
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Typical Uses:
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Used early in the design process
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Used to acquire a rough estimate of reliability
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Telcordia (Bellcore)
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Originally developed at AT&T Bell Labs
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“Modified” MIL-HDBK-217 equations
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New equations represented what their
equipment was experiencing in the field
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Telcordia (Bellcore) (cont.)
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New model with new feature
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Account for “real data”
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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
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Provides models for various types of
mechanical devices including springs,
bearings, seals, etc.
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New and unique standard
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CNET & HRD5
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Used in Europe

Reliability models for telecommunications
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Current Versions:

HRD - 5

CNET - 93
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Bellcore vs. 217

Recognition & Acceptance
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Concentration
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Calculations & Equations
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Consideration of Test Data
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Multiplier

Parts

Environments

Quality Levels
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Accuracy of MTBF
Assessments
Stage I:
Accuracy
Parts count method, assuming
constant failure rates
Stage II:
Variation of failure rates according to
part families
Stage III:
Taking into account of operational
parameters
Stage IV:
Consideration of failure modes,
time influences, different failure
distribution for each part, etc.
Time spent for the analysis
PRISM Reliability
Model

Developed by the Reliability Analysis Center (RAC)

Accounts for the effect of process related variability
on system failure rate
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Inherent failure rate based on base failure rate and
environmental conditions (RAC Rates model)
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Failure rate may then be modified by:

Process Grade Factors, and/or
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Bayesian Analysis, and/or
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Predecessor Data
PRISM Methodology
Operational Profile,
Environmental and
Electrical Stresses
RAC
Component
Models
RAC Failure
Rate Databases
Historical Data
on Similar
Systems
Process
Assessments
Test Data
System Reliability
Assessment
Model
Bayesian
Data
Combination
Software
Model
System
Reliability
Estimate
Primary Causes of Failure
0.087147
0.044514
0.092163
0.124138
0.2
0.094044
1
Software
9%
Parts
22%
No Defect
20%
Manufacturing
15%
Induced
12%
Wearout
9%
Design
System 9%
Management
4%
(Nominal Values)
PRISM Process Grade
Factor Types
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Design
Manufacturing
Parts Quality
System Management
CND (Can Not Duplicate)
Induced
Wearout
Growth
Infant Mortality
Other PRISM Adjustments

Bayesian
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Uses test and field data to enhance
predicted failure rate
Predecessor

Uses previous history data to further refine
predicted failure rate
PRISM Note

Although PRISM contains RAC Rate models for
many part types, it does not include the following:
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Rotating devices
Switching devices
Connections
Miscellaneous parts
Relays
Tubes
Lasers
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.