7/28/2019 3 Rel Models

1/34

RelexReliab i l ity So ftwarethe intuitive solution!

Relex Softw are Corporat ion

1

7/28/2019 3 Rel Models

2/34

2

What is Relex?

A Powerful Reliability Software Tool

performs efficient reliability analysis uses multiple analysis techniques

provides advanced features

7/28/2019 3 Rel Models

3/34

3

Relex Is Uniquely Qual i f ied

Reliability Engineering Experience

Commercial

Military

Software Development Experience

7/28/2019 3 Rel Models

4/34

RelexReliab i l ity So ftwarethe intuitive solution!

Relex Softw are Corporat ion

7/28/2019 3 Rel Models

5/34

Introduction toReliability Prediction

7/28/2019 3 Rel Models

6/34

6

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

7/28/2019 3 Rel Models

7/34

Reliability Measures

Failure Rate ()

Mean Time Between Failures (MTBF)

Reliability

Availability

Samp le Relex Reliabil i ty

Predict io n calculat ion results

7/28/2019 3 Rel Models

8/34

8

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)

7/28/2019 3 Rel Models

9/34

9

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

7/28/2019 3 Rel Models

10/34

10

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)

7/28/2019 3 Rel Models

11/34

11

Availability

Defined As:

The probability that an item is in an

operable state at any time

Units: Probability Value (0-1)

7/28/2019 3 Rel Models

12/34

12

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

7/28/2019 3 Rel Models

13/34

The Bathtub Curve

and Reliability

7/28/2019 3 Rel Models

14/34

14

The Bathtub Curve

Represents failure rate tendencies for

the lifespan of an item Failure rate varies in different phases of

life

7/28/2019 3 Rel Models

15/34

15

Three Phases of Life

Infant Mortality Region

Wear-Out Region

Constant Failure Rate Region

7/28/2019 3 Rel Models

16/34

16

Bathtub Curve

Graph of Failure Rate vs. Time

Considers three phases of life

Represents lifespan of item

(i.e. 15 years for a car)

7/28/2019 3 Rel Models

17/34

Time

FailureRa

te

Infant Mortality

Wear Out

Constant Failure Rate

Bathtub Curve

Illustration

17

7/28/2019 3 Rel Models

18/34

Reliability Models

7/28/2019 3 Rel Models

19/34

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

7/28/2019 3 Rel Models

20/34

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.)

7/28/2019 3 Rel Models

21/34

21

MIL-HDBK-217

Original standard for reliability

Reliability math models electronic devices

Used commercially & in the defense industry

Currently at Revision F Notice 2

7/28/2019 3 Rel Models

22/34

22

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

7/28/2019 3 Rel Models

23/34

23

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

7/28/2019 3 Rel Models

24/34

24

Telcordia (Bellcore) (cont.)

New model with new feature

Account for real data Burn-in, Field, Laboratory testing data

Popular standard for commercial

companies

7/28/2019 3 Rel Models

25/34

25

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

7/28/2019 3 Rel Models

26/34

26

CNET & HRD5

Used in Europe

Reliability models for telecommunications

Current Versions:

HRD - 5

CNET - 93

7/28/2019 3 Rel Models

27/34

27

Bellcore vs. 217

Recognition & Acceptance Concentration

Calculations & Equations

Consideration of Test Data

Multiplier

Parts Environments

Quality Levels

7/28/2019 3 Rel Models

28/34

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

7/28/2019 3 Rel Models

29/34

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

7/28/2019 3 Rel Models

30/34

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

7/28/2019 3 Rel Models

31/34

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)

7/28/2019 3 Rel Models

32/34

PRISM Process Grade

Factor Types

Design

Manufacturing

Parts Quality System Management

CND (Can Not Duplicate)

Induced Wearout

Growth

Infant Mortality

7/28/2019 3 Rel Models

33/34

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

7/28/2019 3 Rel Models

34/34

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.