Electronics Reliability Prediction Electronics Reliability Prediction Using the Product Bill of MaterialsUsing the Product Bill of Materials

Cheryl TulkoffJim Lance

National Instruments

OutlineOutline

Basic Definitions and Background

Case Study

Going Forward

DefinitionsDefinitions

Reliability Prediction– Process used to estimate constant failure rate

( ) of useful product life

DefinitionsDefinitions

MTBF: – Mean Time Between Failures– Reliability of a component or assembly that

can be repaired and put back in service – MTBF = 1/ where = failure rate, typically # of

failing units per million hours

Common MTBF MisconceptionsCommon MTBF Misconceptions

Minimum, guaranteed time between failuresCorrelation between service life & – Can have a very reliable but short-lived

device: missileIncludes assembly and construction factors (quality)

Survival Based on the Survival Based on the Exponential Failure LawExponential Failure Law

Reliability is the probability of zero failures (survival).

Probability Distributions (Exponential, Binomial, Normal, Weibull)

The Exponential Distribution is fairly simple and can get you close with less parameters.

R = exp (-T ) = exp (-T / MTBF)

Example Calculated SurvivalExample Calculated Survival

MTBF Calc AssumptionsMTBF Calc Assumptions

Perfect DesignAll stresses/use data knownFailures are randomAny part failure causes a system failureParts models are up to date and accurate

Reliability Prediction: Industry StandardsReliability Prediction: Industry Standards

Mil Specs–MIL-HDBK-217F

Telcordia (Bellcore) SR-332Prism (System Reliability Center)MixedOthers….

Some Software Providers / OptionsSome Software Providers / Options

RelexReliasoftAsent (Raytheon)RelCalc (T Cubed)LambdaConsultants (Ops A La Carte, DfR, others)

Why try to predict reliability at all?Why try to predict reliability at all?

Compare to competitor’s productsCompare product design from one revision to the nextTool for design improvementIdentify design weaknesses or gaps

Product Case StudyProduct Case Study

Case Study Details– Data Acquisition product in market for

several years with design revisions– Relex Software using 217Plus Model–MTBF calc’d with and without use data

Case Study: MTBF w/o Use DataCase Study: MTBF w/o Use DataCalculation ParametersTemp = 30CTemp Dormant = 23CEnvironment = GSI (Ground Stationary Indoors)Operation Profile = IndustrialDuty Cycle = 100%Vibration Level = 0Cycling Rate = 184

Calculated Failure Rate = 3.46MTBF = 33 yearsProbability of Survival 1 year = 97%

Max Lambda by Component Type

Case Study: MTBF with Use DataCase Study: MTBF with Use Data

Calculation ParametersTemp = 30CTemp Dormant = 23CEnvironment = GSI (Ground Stationary Indoors)Operation Profile = IndustrialDuty Cycle = 100%Vibration Level = 0Cycling Rate = 184

Calculated Failure Rate = 3.06MTBF = 37.3 yearsProbability of Survival 1 year = 97.4%

Max Lambda by Component Type

Case Study: MTBF with Use Data & Case Study: MTBF with Use Data & Duty CycleDuty Cycle

Calculation ParametersTemp = 30CTemp Dormant = 23CEnvironment = GSI (Ground Stationary Indoors)Operation Profile = IndustrialDuty Cycle = 100%Vibration Level = 0Cycling Rate = 184

Calculated Failure Rate = 0.77MTBF = 148 yearsProbability of Survival 1 year = 99.3%

Max Lambda by Component Type

RMA DataRMA Data2004 2005 2006 2007 2008

1165 3157 3282 3052 3113

3 38 24 26 19

99.7% 98.8% 99.3% 99.0% 99.3%

Year

12 Month Base

Returns

% SurvivalOverall Average Survival = 99.2%

Calculated Survival = 99.3%

Issues:Can not be certain of field environments.Not certain actual duty time per unit (Calculations 100% Duty)Out of 19 failures (2008) only 30% had component issues.Other types of failures include (DOA, Calibration, Unknown, etc).Component failures likely use driven (abnormal circuit conditions).

RMA DataRMA Data

Actual Failures versus CalculatedSampled Data from 2008

The ceramic cap was not among the larger calculated lambda components. The failure was among other parts that failed in the circuit most likely due to unusual spike in current during use.

None of the higher lambda components showed up in the data.

= Field Failures

= Calculated Lambda

RecommendationsRecommendations

It is difficult to represent field failures with calculated MTBF models.

It is important for consumers to know how MTBFs were generated and what the limitations are for those calculations.

What next?What next?

Our customers expect us to provide MTBF values for our products. Continue to educate our customers and provide the most consistent numbers we can.Monitor RMA for biggest impact reliability issues from the field.

Closing QuestionsClosing QuestionsHow well does the predicted number match actual product return rates from the field? Does the model predict which components will contribute the most to reliability issues in the field?In our experience, a resounding NO! to both questionsSo, is MTBF good for anything practical?

ReferencesReferences

Reliability for the Technologies Second Edition, Leanard A. Doty, Industrial Press Inc., 1989