Download Reliability Predictions

Reliability Predictions

The objective of a reliability prediction is to
determine if the equipment design will have the
ability to perform its required functions for the
duration of a specified mission profile.

Reliability predictions are usually given in terms
of fails per million hour or Mean Time Between
Failures (MTBF).
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Reliability Predictions

Besides their obvious use to predict reliability,
reliability predictions are used to support many
other analyses such as:
– Spares
– Failure Mode Effects and Criticality Analysis (FMECA)
– Fault Tree
– Warranty
– Performance Based Logistics (PBL)
Why are Spares so important?
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Spares

Why do you need spare boards or boxes? Why not just fix
the ones that fail?
You do fix the ones that fail, but that takes time. The
equipment is unavailable while the repair is made.

Spares allow the equipment to be made available more
quickly.

The Reliability Prediction determines how many spares will
be needed to meet the customers availability requirements.

Operational Availability (Ao) is often a key customer
requirement.
– Ao = System Up Time / Total Time
or
– MTBF/(MTBF + MTTR + MLDT)
MTTR = Mean Time To Repair
MLDT = Mean Logistics Delay Time
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Reliability Predictions

There are many methods to predict the reliability
of a system including:
– MIL-HDBK-217
– Telcordia (Bellcore)
– PRISM
– Physics of Failure
– Comparative Analysis
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Reliability Predictions

MIL-HDBK-217, "Reliability Prediction of
Electronic Equipment”
– The original reliability prediction handbook published
by the Department of Defense, based on work done by
the Reliability Analysis Center and Rome Laboratory
– Contains failure rate models for the various part types
used in electronic systems, such as ICs, transistors,
diodes, resistors, capacitors, relays, switches,
connectors, etc.
– Failure rate models are based on field data obtained
for a wide variety of parts and systems. This data was
analyzed and many simplifying assumptions were
thrown in to create usable models.
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MIL-HDBK-217 includes mathematical reliability
models for nearly all types of electrical and
electronic components. The variables in these
models are parameters of the components such
as number of pins, number of transistors, power
dissipation, and environmental factors.

MIL-HDBK-217 contains two methods of
performing predictions.
– Parts Count - normally used early in the design and is
based on anticipated quantities of parts to be used
– Parts Stress – normally used later in the design and is
based on the stresses applied to each individual part
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
MIL-HDBK-217 Parts Count Prediction
– The general mathematical expression for equipment
failure rate with this method is:
i=n
Equip =  Ni(gQ)i
i=1
Equip = Total equipment failure rate
g = Generic failure rate for the ith generic part
Q = Quality factor for the ith generic part
Ni = Quantity of the ith generic part
n = # of different generic part categories in equipment
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MIL-HDBK-217 Parts Count Prediction Example
– A new RF amplifier board for use in an external pod
mounted radar for a fighter aircraft is anticipated to
use 46 insulated film (RLR, MIL-R-39017) resistors of
established reliability category “R”. Determine the
portion of the failure rate due to these resistors.
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
MIL-HDBK-217 Parts Count Prediction Example
– A new RF amplifier board for use in an external pod
mounted radar for a fighter aircraft is anticipated to
use 47 insulated film (RLR) established reliability level
“R” resistors. Determine the portion of the failure rate
due to these resistors.
Res(RLR) = Ni(gQ)
Res(RLR) = 47 X (.033 X .1)
Res(RLR) = .1551 fails/million hours
NOTE:
This is the failure rate associated with only this type
of resistor. To get the complete failure rate for the
board, the failure rates for all other resistor types and
for all other components would have to be added.
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Reliability Predictions
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MIL-HDBK-217 Parts Stress Prediction
– For this method, different types of parts (resistors,
capacitors, microcircuits, etc.) and different classes of
parts of the same type (memory, microprocessors,
etc.) have different failure rate equations.
– A separate failure rate is determined for each part
based on the stresses applied to that part. These
failure rates are added to determine the total failure
rate for the unit being analyzed.
Fixed Film
Resistor
p = b RQ E
DRAM
Microprocessor
p = (C1T + C2E)Q L
p = (C1T + C2E + cyc)Q L
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MIL-HDBK-217 Parts Stress Prediction Example
– Reference Designator R6 (133 ohm, RLR, MIL-R-39017,
established reliability level “R”) on an RF amplifier
board for use in an external pod mounted radar for a
fighter aircraft has been shown to operate at 48 C at
30% of its rated power. Determine the portion of the
failure rate due to this resistor.
MIL-HDBK-217 Parts Stress equation for this type of
part is:
p = b RQ E
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
MIL-HDBK-217 Parts Stress Prediction Example
– Reference Designator R6 (133 ohm, RLR, MIL-R-39017,
established reliability level “R”) on an RF amplifier
board for use in an external pod mounted radar for a
fighter aircraft has been shown to operate at 48 C at
30% of its rated power. Determine the portion of the
failure rate due to this resistor.
R6 = b RQ E
b = .0011
R = 1.0
Q = 0.1
E = 18
R6 = .0011 X 1.0 X 0.1 X 18 = .00198 fails/million hours
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
Telcordia (Bellcore)
– Originally developed by Bell Labs
– Bell Labs modified the equations in MIL-HDBK-217 to
better represent what their equipment was
experiencing in the field.
– Tends to be a lot more forgiving of nonmilitary parts
than MIL-HDBK-217
– Methodology is very similar to MIL-HDBK-217 – If you
know how to use one, you can use the other.
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
Now for the Bad News
– Opinions of both of these methods (MIL-HDBK-217 and
Telcordia) are very low in many quarters.
– Both have very poor track records predicting actual field
performance though they may be useful in making
comparisons between competing system designs.

The biggest strength of both of these methods is that they
provide a recognized systematic methodology which
minimizes the need to make “judgments”; however, …

This strength lasts only as long as customers continue to
“recognize” these methods as valid and this situation is
changing with some customers prohibiting their use. In
addition, whether “recognized” or not, the basic problem
remains - these methodologies provide poor answers for
a critical question.
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PRISM
– Modification of MIL-STD-217
– Attempt by RAC to overcome some of MIL-STD-217’s
problems
– Does not include models for all commonly used
devices
– Provides the ability to update predictions based on
test data
– Addresses factors such as development process
robustness
– Values of individual factors are determined through an
extensive question/answer process to judge the extent
that measures known to enhance reliability are used in
design, manufacturing and management processes.
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PRISM
•
PRISM software reliability prediction tool developed by the
Reliability Analysis Center (RAC)
•
PRISM accounts for failure sources in addition to part failures
– Mil-HDBK-217 and Telcordia
address only part failures
– PRISM introduces the use of
“process grades”
– PRISM allows 2 types of
predictions
•
•

Inherent reliability

Logistics model
PRISM uses a model consisting
of additive and multiplicative
terms
Based on failures/106 calendar
hrs
– Clndr Hrs = Op Hrs / Duty Cycle
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Software
9%
Sys Mgmt
4%
Part Defect
22%
Wearout
9%
Design
9%
No Defect
12%
Induced
20%
Mfg Defect
15%
Failure Cause Distribution for
Electrical Systems
(Based on RAC Survey)
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PRISM

Sys Fail Rate = (S component failure rates) x process grade factor
– RACRate model
– Process grades in 9 areas
•
Microcircuits
• Transistors
•
Diodes
• Thyristors
•
Capacitors
• Resistors
•
Software
•
•
•
•
•
Parts
• Design
Induced
• System Mgmt.
No-defect • Manufacturing
Wearout
• Infant Mortality
Reliability Growth
– RAC data
•
Electronics Parts Reliability Data
•
Nonelectronic Parts Reliability Data
– User-defined data
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PRISM Failure Rate Models
P(Inherent) = IA (PPPIMPE + PDPG + PMPIMPEPG + PSPG + PW) + SW
Parts
Design
Manufacturing
System
Mgmnt
Wearout
Software not
included in
Raytheon
evaluation
P(Logistics) = IA (PPPIMPE + PDPG + PMPIMPEPG + PSPG + PI + PN + PW) + SW
System-level process grade multiplier
(approximately 1.0 for “average” processes)
Failure Rate / Definition
P
Predicted failure rate of the system
IA
Initial failure rate assessment (sum of RACRates, RAC data, and user defined data)
SW
Software failure rate (RACRates software model, RAC Data, or user-defined data)
Process Multiplier / Definition
PP
Parts process grade factor
PM
Manufacturing process grade factor
PIM
PRISM infant mortality model
PS
System management process grade factor
PE
PRISM environmental factor
PI
Induced process grade factor
PD
Design process grade factor
PN
No-defect process grade factor
PG
PRISM reliability growth model
PW
Wearout process grade factor
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Physics of Failure (PoF)
– Attempt to identify the "weakest link" of a design to ensure
that the required equipment life is exceeded
– Generally ignores the issue of manufacturing defect escapes
and assumes that product reliability is strictly governed by
the predicted life of the weakest link
– Models are very complex and require detailed device
geometry information and materials properties
– In general, the models are more useful in the early stages of
designing components, but not at the assembly level.
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Comparative Analysis

Predictions based on field data for similar
products can be very useful, but suffer from the
following problems.
– Accurate field data is often not available
– Usually requires making engineering judgments (to
compensate for different operating environment,
failures that now have C/A in place, etc.)
This is the preferred method if good data exists.
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Comparative Analysis
Example:
MTBF Prediction for ABX Radar
WRA
MTBF
Source
ANT
3210 APA Radar
PPS
4513 APA Radar
SDC
6200 APA Radar
RSCI
1300 ZPAR Radar
XMTR
1815 APA Radar
REP
1021
X20 radar
RSC
1100
X20 radar
ABX Radar System 256
Rollup
WRA
ANT
PPS
SDC
RSCI
XMTR
REP
ABA Radar System
MTBF
3210
4513
6200
6212
1513
985
395
Source
Field
Field
Field
Field
Field
Field
Rollup
Modified by removing failures
with C/A in place.
Modified to account for more
severe ABX environment.
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WRA
ANT
PPS
SDC
RSCI
XMTR
ZPAR Radar System
MTBF
3400
3500
6200
1300
675
334
Source
Field
Field
Field
Field
Field
Rollup
WRA
AESA
PPS
REP
RSC
PROC
X20 Radar System
MTBF
3400
3500
1513
1324
6200
464
Source
Field
Field
Field
Field
Field
Rollup
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Reliability Predictions

Summary
– A number of different methods exist for predicting
reliability.
– No prediction method is without its problems.
– The Reliability Engineer together with the
supportability IPT must pick the best method or
combination of methods for his program.
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