Bayesian Networks Analyse LED Reliability
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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.
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Bayesian Networks Analyse LED Reliability
- 1. jan_eite.bullema@tno.nl Bayesian Networks to analyse LED reliability An introduction Jan Eite Bullema – ASQ Certified Reliability Engineer
- 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
- 5. jan_eite.bullema@tno.nl The bath-tub curve Reliability Life-Model Bayesian Networks for analysis of LED reliability 5 Source: R. Crowe, Design for Reliability, CRC Press 2001
- 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