1) Bayesian networks can be used to analyze LED reliability by building models from expert knowledge of failure modes.
2) LED systems are complex with reliability dependent on factors beyond single component performance like temperature effects.
3) Bayesian networks combined with mission profiles can predict reliability by considering real world usage conditions over time.
2. jan_eite.bullema@tno.nl
Outline
Bayesian Networks for Analysis of LED reliability
Bayesian Networks for analysis of LED reliability
2
Overview of Reliability Science
What are Bayesian Networks?
Challenges in LED System complexity
Limitations in part based lifetime predictions
Modern reliability approaches,
e.g. MOEST, Bayesian Networks, Big Data
Conclusions
3. jan_eite.bullema@tno.nl
What is Reliability
Definitions of Quality and Reliability
Bayesian Networks for analysis of LED reliability
3
1996, Lewis
Quality
The ability of a product to fulfil its intended purpose
Reliability
The ability of a product to fulfil its intended purpose
for a certain period of time under stated conditions
4. jan_eite.bullema@tno.nl
Why is Reliability Important?
Business Issues
Bayesian Networks for analysis of LED reliability
4
---------------
Reputation
Customer Satisfaction
Warranty Costs
Repeat Business
Cost Analysis
Customer Requirements
Competitive Advantage
6. jan_eite.bullema@tno.nl
What is a Bayesian Network?
Bayesian Networks are based upon Bayes Theorem
Bayesian Networks for analysis of LED reliability
6
Source: Bayesian Statistics – a brief introduction, Ken Rice, 2014
Common sense reduced to computation
Pierre-Simon, marquis de Laplace (1749-1827)
Inventor of Bayesian inference
7. jan_eite.bullema@tno.nl
What is a Bayesian Network?
A Bayesian Network is a Directed Acyclic Graph
Bayesian Networks for analysis of LED reliability
7
Source: TNO – report Dynamic Bayesian Network simulations of a lighting system
8. jan_eite.bullema@tno.nl
What is a Bayesian Network?
Bayesian Network built from Expert Knowledge
Step by Step Example for a simple LED system
Bayesian Networks for analysis of LED reliability
8
Source: TNO – report Dynamic Bayesian Network simulations of a lighting system
Step 1. System Decomposition
9. jan_eite.bullema@tno.nl
What is a Bayesian Network?
Bayesian Network built from Expert Knowledge
Step 2. Failure Mode and Effect Analysis
Bayesian Networks for analysis of LED reliability
9
Source: TNO – report Dynamic Bayesian Network simulations of a lighting system
10. jan_eite.bullema@tno.nl
What is a Bayesian Network?
Bayesian Network built from Expert Knowledge
Step 3. Failure Tree from FMEA
Bayesian Networks for analysis of LED reliability
10
Source: TNO – report Dynamic Bayesian Network simulations of a lighting system
11. jan_eite.bullema@tno.nl
What is a Bayesian Network?
Bayesian Network built from Expert Knowledge
Step 4. Translate Failure Tree for computation
Bayesian Networks for analysis of LED reliability
11
Source: TNO
Step 5. Calculate Physics of Failure
A_SOL_1 <- exp(Ea_SOL/KB*(1/TE-1/T_SOL_1))
R_SOL_1 <- exp(-TIME * A_SOL_1 * (10^9/FIT_SOL)^-1)
Step 6. Do a Monte Carlo Simulation
12. jan_eite.bullema@tno.nl
What is a Bayesian Network?
Bayesian Network built from Expert Knowledge
Step 7. Do Bayesian Inference
Bayesian Networks for analysis of LED reliability
12
Source: TNO
Step 8. Calculate Reliability Predictions
13. jan_eite.bullema@tno.nl
What is a Bayesian Network?
Bayesian Network built from Expert Knowledge
Step 9. Repeat steps 5 - 8 over a time window
Bayesian Networks for analysis of LED reliability
13
Source: TNO
Step 10. Analyse Reliability Predictions
14. jan_eite.bullema@tno.nl
Outline
Bayesian Networks for Analysis of LED reliability
Bayesian Networks for analysis of LED reliability
14
Overview of Reliability Science
What are Bayesian Networks?
Challenges in LED reliability
i.e. Lifetimes > 50.000 hrs.
Modern reliability approaches,
e.g. MOEST, Bayesian Networks, Big Data
Conclusions
15. jan_eite.bullema@tno.nl
Reliability Basics
What is the reliability of a single LED
Bayesian Networks for analysis of LED reliability
15
http://rl.omslighting.com/ledacademy/694/led-academy
LED manufacturer claims that
50% of their LEDs (from any
batch) will emit at least 70% of
the initial lumens after 50,000
hours of operation
16. jan_eite.bullema@tno.nl
Reliability Basics
What is the reliability of a single LED
E.g. the Mean Time Between Failures
Bayesian Networks for analysis of LED reliability
16
ETAP White Paper: LED Lifetime in Practice
17. jan_eite.bullema@tno.nl
Reliability Basics
What is the reliability of a single LED
e.g. Temperature Dependence of MTBF
Bayesian Networks for analysis of LED reliability
17
LumiLED High Flux High Power Reliability Data
18. jan_eite.bullema@tno.nl
Reliability Basics
What is the reliability of a single LED?
e.g. Temperature Dependence of MTBF
Bayesian Networks for analysis of LED reliability
18
LumiLED High Flux High Power Reliability Data
19. jan_eite.bullema@tno.nl
Durability of LED Systems
Lifetimes > 50.000 hrs and Mission Profiles
Bayesian Networks for analysis of LED reliability
19
Life time of a LED System has to be 25 000 – 50 000 hrs.
Gielen et al, Development of an intelligent integrated LED system-in-package, EPMC 2011
20. jan_eite.bullema@tno.nl
Durability of LED Systems
System behaviour is more than the sum of
component behaviour
Bayesian Networks for analysis of LED reliability
20
http://www.ledsmagazine.com
21. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Bayesian Networks for analysis of LED reliability
21
Bullema, Combination of Bayesian Networks
and FEM models to Predict Reliability of LED Systems, ECTS 2012
Example: The ESIP Demonstrator:
22. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Bayesian Networks for analysis of LED reliability
22
Bullema, Combination of Bayesian Networks
and FEM models to Predict Reliability of LED Systems, ECTS 2012
Example: The CSSL Demonstrator:
23. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Bayesian Networks for analysis of LED reliability
23
Bullema, Combination of Bayesian Networks
and FEM models to Predict Reliability of LED Systems, ECTS 2012
Example: The ESIP Demonstrator:
compact LED system with integrated driver and string of six LEDs
LED
die
Driver
chip
Thermal
Pad
IO leads
Silicone
lens/filler
Moulding
compound
LED
die
Driver
chip
Thermal
Pad
IO leads
Silicone
lens/filler
Moulding
compound
24. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Bayesian Networks for analysis of LED reliability
24
Bullema, Combination of Bayesian Networks
and FEM models to Predict Reliability of LED Systems, ECTS 2012
Example: The ESIP Demonstrator:
25. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Bayesian Networks for analysis of LED reliability
25
Bullema, Combination of Bayesian Networks
and FEM models to Predict Reliability of LED Systems, ECTS 2012
Example: The ESIP Demonstrator:
Failure tree for electrical defects and reduced lumen output
26. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Bayesian Networks for analysis of LED reliability
26
Bullema, Combination of Bayesian Networks
and FEM models to Predict Reliability of LED Systems, ECTS 2012
Example: The ESIP Demonstrator:
Calculation R(t) for not cooled and heat pipe cooled (T = T not cooled - 20 C) Design
ESIP with and
without heat pipe
27. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Bayesian Networks for analysis of LED reliability
27
Bullema, Combination of Bayesian Networks
and FEM models to Predict Reliability of LED Systems, ECTS 2012
Example: The ESIP Demonstrator:
Predicted Survival Rate R(t) of LV @ 25 ºC
Continuous,1xSwitch/day,10xSwitch/day
Green line = 10 x switching / day
Red line = 1 x switched / day
Blue line = No switching
28. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Bayesian Networks for analysis of LED reliability
28
Bullema, Combination of Bayesian Networks
and FEM models to Predict Reliability of LED Systems, ECTS 2012
Example: The ESIP Demonstrator:
ESIP with and
without heat pipe
CSSL Design A, B
At Low and High Temp
29. jan_eite.bullema@tno.nl
LED Systems are Complex
Knowing the behaviour of parts is not sufficient
Use Mission Profiles to Predict Reliability
Bayesian Networks for analysis of LED reliability
29
Source: TNO report
30. jan_eite.bullema@tno.nl
Outline
Bayesian Networks for Analysis of LED reliability
Bayesian Networks for analysis of LED reliability
30
-------------------------
Overview of Reliability Science
What are Bayesian Networks?
Challenges in LED reliability
i.e. Lifetimes > 50.000 hrs.
Modern reliability approaches,
e.g. MOEST, Bayesian Networks, Big Data
Conclusions
31. jan_eite.bullema@tno.nl
MEOST: a jump into the future
Multiple Over Stress Testing: finding the weak spot
Bayesian Networks for analysis of LED reliability
31
Keki R. Bhote , World Class Reliability, ISBN 0-8144-0792-7
Various stresses and their action
32. jan_eite.bullema@tno.nl
The Future lies in use of Big Data
Measuring all lamps real time during using
advanced prognostics: all the tools are available
Bayesian Networks for analysis of LED reliability
32
http://www.smartindustry.nl/
33. jan_eite.bullema@tno.nl
Approach to develop Reliability Model based
upon a Bayesian Network
Bayesian Networks for analysis of LED reliability
33
Bullema, Combination of Bayesian Networks and FEM models to Predict Reliability of LED Systems, 2012
Editor's Notes
Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase, and defense budgets decrease.
Reliability has been a recognized performance factor for at least 50 years. During World War II, the V-1 missile team, led by Dr. Wernher von Braun, developed what was probably the first reliability model. The model was based on a theory advanced by Eric Pieruschka that if the probability of survival of an element is 1/x, then the probability that a set of n identical elements will survive is (1/x)n . The formula derived from this theory is sometimes called Lusser’s law (Robert Lusser is considered a pioneer of reliability) but is more frequently known as the formula for the reliability of a series system: Rs = R1 x R2 x . . x Rn.
Bayesian Networks are derived from Bayes Theorem -
A Bayesian Network is a so-called directed acyclic graph. Basically Bayesian Networks are used to represent knowledge and the nice thing is that you can calculate with these networks and update the networks as your knowledge – based upon new insights or new measurements- increases
In mathematics and computer science, a directed acyclic graph (, is a directed graph with no directed cycles. That is, it is formed by a collection of vertices and directed edges, each edge connecting one vertex to another, such that there is no way to start at some vertex v and follow a sequence of edges that eventually loops back to v again.[1][2][3]
DAGs may be used to model many different kinds of information.
A Bayesian Network is a so-called directed acyclic graph. Basically Bayesian Networks are used to represent knowledge and the nice thing is that you can calculate with these networks and update the networks as your knowledge – based upon new insights or new measurements- increases
A Bayesian Network is a so-called directed acyclic graph. Basically Bayesian Networks are used to represent knowledge and the nice thing is that you can calculate with these networks and update the networks as your knowledge – based upon new insights or new measurements- increases
A Bayesian Network is a so-called directed acyclic graph. Basically Bayesian Networks are used to represent knowledge and the nice thing is that you can calculate with these networks and update the networks as your knowledge – based upon new insights or new measurements- increases
A Bayesian Network is a so-called directed acyclic graph. Basically Bayesian Networks are used to represent knowledge and the nice thing is that you can calculate with these networks and update the networks as your knowledge – based upon new insights or new measurements- increases
A Bayesian Network is a so-called directed acyclic graph. Basically Bayesian Networks are used to represent knowledge and the nice thing is that you can calculate with these networks and update the networks as your knowledge – based upon new insights or new measurements- increases
A Bayesian Network is a so-called directed acyclic graph. Basically Bayesian Networks are used to represent knowledge and the nice thing is that you can calculate with these networks and update the networks as your knowledge – based upon new insights or new measurements- increases
It isn't just about the LED. Good LEDs can be incorporated into poorly engineered products and turn the Methuselah of lighting into the exponent of “live fast, die young.” The promise of LED lifetime is often presented in terms of hours and years but with little background data to support anything beyond vacuous promises. The statement of 100,000 hours of LED luminaire lifetime has given way to the realization that there is little consistency, very little published data, and few hard facts around so‐called luminaire lifetime numbers. The situation is better at the LED package level, where reputable manufacturers have thousands of hours of data under varying conditions. But this
is not enough.
De onbemande ruimtesonde Voyager 2 werd op 20 augustus 1977 vanaf Cape Canaveral, Florida gelanceerd aan boord van een Titan III raket. Het oorspronkelijke doel van Voyager 1 en 2 was het van dichtbij onderzoeken van Jupiter, Saturnus, de ringen van Saturnus en de grootste manen van beide planeten. Indien mogelijk - de sondes waren gebouwd om vijf jaar operationeel te zijn - zou Voyager 2 echter ook nog Uranus en Neptunus aandoen.
Op 22 april 2010 ontstond er een probleem met de communicatie tussen aarde en de Voyager 2. De 33 jaar oude Voyager die zich nu op ruim 14 miljard kilometer ( 14.000.000.000 km ) bevindt stuurde signalen naar de aarde die niet te decoderen waren. De Voyager werkte naar eigen zeggen nog goed. Nasa heeft het probleem inmiddels verholpen. Makkelijk was dat niet aangezien een commando vanaf de aarde er 13 uur over doet om de Voyager te bereiken. Nadat de Voyager drie weken in "spaarstand" heeft gestaan bleek de oorzaak in het computergeheugen te zitten. Een enkel bitje was spontaan van 0 naar 1 gesprongen. Nasa heeft de computer van de sonde gereset en de sonde lijkt nu weer naar behoren te werken