Common MTBF Misconceptions 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.

1. Electronics Reliability Prediction
Using the Product Bill of Materials
Cheryl Tulkoff
Jim Lance
National Instruments

2. Outline
z Basic Definitions and Background
z Case Study
z Going Forward

3. Definitions
z Reliability Prediction
– Process used to estimate constant failure rate
(Ȝ) of useful product life

4. Definitions
z MTBF:
– Mean Time Between Failures
– Reliability of a component or assembly that
can be repaired and put back in service
– MTBF = 1/Ȝ where Ȝ = failure rate, typically # of
failing units per million hours

5. Common MTBF Misconceptions
z Minimum, guaranteed time between
failures
z Correlation between service life & Ȝ
– Can have a very reliable but short-lived
device: missile
z Includes assembly and construction
factors (quality)

6. Survival Based on the
Exponential Failure Law
z Reliability is the probability of zero failures
(survival).
z Probability Distributions (Exponential, Binomial,
Normal, Weibull)
z The Exponential Distribution is fairly simple and
can get you close with less parameters.
R = exp (-T Ȝ) = exp (-T / MTBF)

7. Example Calculated Survival

8. MTBF Calc Assumptions
z Perfect Design
z All stresses/use data known
z Failures are random
z Any part failure causes a system
failure
z Parts models are up to date and
accurate

9. Reliability Prediction: Industry Standards
z Mil Specs
– MIL-HDBK-217F
z Telcordia (Bellcore) SR-332
z Prism (System Reliability Center)
z Mixed
z Others….

10. Some Software Providers / Options
z Relex
z Reliasoft
z Asent (Raytheon)
z RelCalc (T Cubed)
z Lambda
z Consultants (Ops A La Carte, DfR,
others)

11. Why try to predict reliability at all?
z Compare to competitor’s products
z Compare product design from one
revision to the next
z Tool for design improvement
z Identify design weaknesses or gaps

12. Product Case Study
z 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

13. Case Study: MTBF w/o Use Data
Calculation Parameters
Temp = 30C
Temp Dormant = 23C
Environment = GSI (Ground Stationary Indoors)
Operation Profile = Industrial
Duty Cycle = 100%
Vibration Level = 0
Cycling Rate = 184
Calculated Failure Rate = 3.46
MTBF = 33 years
Probability of Survival 1 year = 97%
Max Lambda by
Component Type

14. Case Study: MTBF with Use Data
Calculation Parameters
Temp = 30C
Temp Dormant = 23C
Environment = GSI (Ground Stationary Indoors)
Operation Profile = Industrial
Duty Cycle = 100%
Vibration Level = 0
Cycling Rate = 184
Calculated Failure Rate = 3.06
MTBF = 37.3 years
Probability of Survival 1 year = 97.4%
Max Lambda by
Component Type

15. Case Study: MTBF with Use Data &
Duty Cycle
Calculation Parameters
Temp = 30C
Temp Dormant = 23C
Environment = GSI (Ground Stationary Indoors)
Operation Profile = Industrial
Duty Cycle = 100%
Vibration Level = 0
Cycling Rate = 184
Calculated Failure Rate = 0.77
MTBF = 148 years
Probability of Survival 1 year = 99.3%
Max Lambda by
Component Type

16. RMA Data
2004 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
% Survival
Overall 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).

17. RMA Data
Sampled Data from 2008 Actual Failures versus Calculated
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

18. Recommendations
z It is difficult to represent field failures
with calculated MTBF models.
z It is important for consumers to
know how MTBFs were generated
and what the limitations are for those
calculations.

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

20. Closing Questions
z How well does the predicted number match actual
product return rates from the field?
z Does the model predict which components will
contribute the most to reliability issues in the
field?
z In our experience, a resounding NO! to both
questions
z So, is MTBF good for anything practical?
References
z Reliability for the Technologies Second Edition, Leanard A. Doty,
Industrial Press Inc., 1989