SlideShare une entreprise Scribd logo
1  sur  7
Télécharger pour lire hors ligne
Prediction of Failure Rates




 Lars Rimestad, 2009-03-30
                              1
History of Reliability
     US Dept. Of Defence: Military systems => extreme failure rates
                                                  AGREE (1952):
50


                                                  •Reliability = integral part of development
19




                                                  •Derating
                                                  •Test @ high Stress @ 1000’s of hours
          60

                                                  •MTBF & Statistics
        19




                              70
                           19



                                                     80
                                                  19



                                                                              90
                                                                        19



                                                                                   00
                                                                                   20
                                                                                                2
               AGREE: Advisory Group on Reliability of Electronic Equipment
History of Reliability


                     MIL-HDBK-217 (1962) started
50
19




                              •Failure rate is constant (λ)
       60
      19


                              •MTBF = 1/ λ
               70
              19
                                    MIL-HDBK-217 cancelled


                         80
                     19



                                   90
                                  19

                                                   t.  2
                                                no




                                                           00
                                                : F-
                                            -2 8




                                                           20
                                        - 02
                                                                3
                                       95
                                       19
Predicting the failure rate, MIL-HDBK-217
Electrolytic capacitors:
λP = λbπ CV π Qπ E Failures / 10 hours
                              6


Microprocessors:
λP = (C1π T + C2π E )π Qπ L Failures / 10 hours
                                         6
     •
     •
     •

 For the entire system:
                i =n
                                             1
 λSYSTEM = ∑ λP ,i ⇒ MTBFSystem =
                i =1                     λSystem
                                                   4
Necessary assumptions for prediction:
•Constant failure rate
   •1 Toyota for 7.000 hours = 7.000 Toyota’s for 1 hour
   •A new car fails just as often as an old car that has run 300.000 km
•System = sum of its components
•No tolerance problems. No interface problems.
•SW quality doesn’t matter!
   •1 line-of-code identical to 70.000 lines-of-code
•Mechanical quality doesn’t matter!
   •However, you can use NPRD-95 (Non-electronic Parts Reliability Data), which is
   much less refined than MIL-HDBK-217
•All system manufacturers have identical production quality
•Only 14 different environments (of which 11 are military)
•The authors of MIL-HDBK-217 know the quality of your product.
                                                                                     5
Basic belief for the use of MIL-HDBK-217
- And it’s many sisters: HRD-5, RDF-2000, Italtel, Telcordia/Bellcore …


 •Product reliability is inherent in the components. When a
 component fails, the cause should be found in the
 component itself.
 •This was more true in the 1960’ies when the failure
 pattern was caused by many electronic component defects,
 due to low production quality.
 •Today, this viewpoint is obsolete.

Grundfos does not use this type of reliability prediction.
                                                                          6
”To meet any reliability objective requires comprehensive
knowledge of the interaction of failure mechanisms, failure
modes, the mission profile, and the design of the product.”

                                  J1879, Handbook for robustness
                                  validation of semiconductor devices
                                  in automotive applications




                                                                        7

Contenu connexe

Similaire à Prediction of-failure-rates-2009-03-30-v01

Datasheet sensor temperatura mcp9700
Datasheet sensor temperatura mcp9700Datasheet sensor temperatura mcp9700
Datasheet sensor temperatura mcp9700
ADELIUS
 
Applied motion products si5580 datasheet
Applied motion products si5580 datasheetApplied motion products si5580 datasheet
Applied motion products si5580 datasheet
Electromate
 
Drvg_HB_LED_HP Ind_Light Fix
Drvg_HB_LED_HP Ind_Light FixDrvg_HB_LED_HP Ind_Light Fix
Drvg_HB_LED_HP Ind_Light Fix
Steve Mappus
 
04.ppt grd200 introduction 2020_july
04.ppt grd200 introduction 2020_july04.ppt grd200 introduction 2020_july
04.ppt grd200 introduction 2020_july
Ngoc Tran
 
Ricardo DC-DC Converter Presentation for NMI
Ricardo DC-DC Converter Presentation for NMIRicardo DC-DC Converter Presentation for NMI
Ricardo DC-DC Converter Presentation for NMI
Frank Warnes
 

Similaire à Prediction of-failure-rates-2009-03-30-v01 (20)

8 inch TFT-LCD Datesheet, AUO, 800*1280, MIPI Interface
8 inch TFT-LCD Datesheet, AUO, 800*1280, MIPI Interface8 inch TFT-LCD Datesheet, AUO, 800*1280, MIPI Interface
8 inch TFT-LCD Datesheet, AUO, 800*1280, MIPI Interface
 
Lewis Chu,Marketing Director,GUC
Lewis Chu,Marketing Director,GUC Lewis Chu,Marketing Director,GUC
Lewis Chu,Marketing Director,GUC
 
Datasheet sensor temperatura mcp9700
Datasheet sensor temperatura mcp9700Datasheet sensor temperatura mcp9700
Datasheet sensor temperatura mcp9700
 
How to make a Line Follower Robot
How to make a Line Follower RobotHow to make a Line Follower Robot
How to make a Line Follower Robot
 
Rf technology 5-8-2011-final-revised
Rf technology 5-8-2011-final-revisedRf technology 5-8-2011-final-revised
Rf technology 5-8-2011-final-revised
 
Product Brochure
Product BrochureProduct Brochure
Product Brochure
 
S10 tr-tank rupturetutorial
S10 tr-tank rupturetutorialS10 tr-tank rupturetutorial
S10 tr-tank rupturetutorial
 
QuickSilver Controls QCI-DS021 QCI-D2-IGF
QuickSilver Controls QCI-DS021 QCI-D2-IGFQuickSilver Controls QCI-DS021 QCI-D2-IGF
QuickSilver Controls QCI-DS021 QCI-D2-IGF
 
10.1 inch-1200x1920-mipi-interface-ips-lcd-module
10.1 inch-1200x1920-mipi-interface-ips-lcd-module10.1 inch-1200x1920-mipi-interface-ips-lcd-module
10.1 inch-1200x1920-mipi-interface-ips-lcd-module
 
RAF Tabtronics LLC Overview 2013
RAF Tabtronics LLC Overview 2013RAF Tabtronics LLC Overview 2013
RAF Tabtronics LLC Overview 2013
 
ScilabTEC 2015 - Xilinx
ScilabTEC 2015 - XilinxScilabTEC 2015 - Xilinx
ScilabTEC 2015 - Xilinx
 
Applied motion products si5580 datasheet
Applied motion products si5580 datasheetApplied motion products si5580 datasheet
Applied motion products si5580 datasheet
 
NXP TP-TD-AUT205 sfjkdgsj sfskfsf sdfsff
NXP TP-TD-AUT205 sfjkdgsj sfskfsf sdfsffNXP TP-TD-AUT205 sfjkdgsj sfskfsf sdfsff
NXP TP-TD-AUT205 sfjkdgsj sfskfsf sdfsff
 
Drvg_HB_LED_HP Ind_Light Fix
Drvg_HB_LED_HP Ind_Light FixDrvg_HB_LED_HP Ind_Light Fix
Drvg_HB_LED_HP Ind_Light Fix
 
QuickSilver Controls QCI-DS017 QCI-M11
QuickSilver Controls QCI-DS017 QCI-M11QuickSilver Controls QCI-DS017 QCI-M11
QuickSilver Controls QCI-DS017 QCI-M11
 
Presentation 1
Presentation 1Presentation 1
Presentation 1
 
04.ppt grd200 introduction 2020_july
04.ppt grd200 introduction 2020_july04.ppt grd200 introduction 2020_july
04.ppt grd200 introduction 2020_july
 
High_Low_Impedance_BusBar_Protection.ppt
High_Low_Impedance_BusBar_Protection.pptHigh_Low_Impedance_BusBar_Protection.ppt
High_Low_Impedance_BusBar_Protection.ppt
 
21955068-High-Low-Impedance-BusBar-Protection.ppt
21955068-High-Low-Impedance-BusBar-Protection.ppt21955068-High-Low-Impedance-BusBar-Protection.ppt
21955068-High-Low-Impedance-BusBar-Protection.ppt
 
Ricardo DC-DC Converter Presentation for NMI
Ricardo DC-DC Converter Presentation for NMIRicardo DC-DC Converter Presentation for NMI
Ricardo DC-DC Converter Presentation for NMI
 

Plus de Accendo Reliability

Reliability Programs
Reliability ProgramsReliability Programs
Reliability Programs
Accendo Reliability
 

Plus de Accendo Reliability (20)

Should RCM be applied to all assets.pdf
Should RCM be applied to all assets.pdfShould RCM be applied to all assets.pdf
Should RCM be applied to all assets.pdf
 
T or F Must have failure data.pdf
T or F Must have failure data.pdfT or F Must have failure data.pdf
T or F Must have failure data.pdf
 
Should RCM Templates be used.pdf
Should RCM Templates be used.pdfShould RCM Templates be used.pdf
Should RCM Templates be used.pdf
 
12-RCM NOT a Maintenance Program.pdf
12-RCM NOT a Maintenance Program.pdf12-RCM NOT a Maintenance Program.pdf
12-RCM NOT a Maintenance Program.pdf
 
13-RCM Reduces Maintenance.pdf
13-RCM Reduces Maintenance.pdf13-RCM Reduces Maintenance.pdf
13-RCM Reduces Maintenance.pdf
 
11-RCM is like a diet.pdf
11-RCM is like a diet.pdf11-RCM is like a diet.pdf
11-RCM is like a diet.pdf
 
09-Myth RCM only product is maintenance.pdf
09-Myth RCM only product is maintenance.pdf09-Myth RCM only product is maintenance.pdf
09-Myth RCM only product is maintenance.pdf
 
10-RCM has serious weaknesses industrial environment.pdf
10-RCM has serious weaknesses industrial environment.pdf10-RCM has serious weaknesses industrial environment.pdf
10-RCM has serious weaknesses industrial environment.pdf
 
08-Master the basics carousel.pdf
08-Master the basics carousel.pdf08-Master the basics carousel.pdf
08-Master the basics carousel.pdf
 
07-Manufacturer Recommended Maintenance.pdf
07-Manufacturer Recommended Maintenance.pdf07-Manufacturer Recommended Maintenance.pdf
07-Manufacturer Recommended Maintenance.pdf
 
06-Is a Criticality Analysis Required.pdf
06-Is a Criticality Analysis Required.pdf06-Is a Criticality Analysis Required.pdf
06-Is a Criticality Analysis Required.pdf
 
05-Failure Modes Right Detail.pdf
05-Failure Modes Right Detail.pdf05-Failure Modes Right Detail.pdf
05-Failure Modes Right Detail.pdf
 
03-3 Ways to Do RCM.pdf
03-3 Ways to Do RCM.pdf03-3 Ways to Do RCM.pdf
03-3 Ways to Do RCM.pdf
 
04-Equipment Experts Couldn't believe response.pdf
04-Equipment Experts Couldn't believe response.pdf04-Equipment Experts Couldn't believe response.pdf
04-Equipment Experts Couldn't believe response.pdf
 
02-5 RCM Myths Carousel.pdf
02-5 RCM Myths Carousel.pdf02-5 RCM Myths Carousel.pdf
02-5 RCM Myths Carousel.pdf
 
01-5 CBM Facts.pdf
01-5 CBM Facts.pdf01-5 CBM Facts.pdf
01-5 CBM Facts.pdf
 
Lean Manufacturing
Lean ManufacturingLean Manufacturing
Lean Manufacturing
 
Reliability Engineering Management course flyer
Reliability Engineering Management course flyerReliability Engineering Management course flyer
Reliability Engineering Management course flyer
 
How to Create an Accelerated Life Test
How to Create an Accelerated Life TestHow to Create an Accelerated Life Test
How to Create an Accelerated Life Test
 
Reliability Programs
Reliability ProgramsReliability Programs
Reliability Programs
 

Dernier

Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 

Dernier (20)

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation StrategiesHTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 

Prediction of-failure-rates-2009-03-30-v01

  • 1. Prediction of Failure Rates Lars Rimestad, 2009-03-30 1
  • 2. History of Reliability US Dept. Of Defence: Military systems => extreme failure rates AGREE (1952): 50 •Reliability = integral part of development 19 •Derating •Test @ high Stress @ 1000’s of hours 60 •MTBF & Statistics 19 70 19 80 19 90 19 00 20 2 AGREE: Advisory Group on Reliability of Electronic Equipment
  • 3. History of Reliability MIL-HDBK-217 (1962) started 50 19 •Failure rate is constant (λ) 60 19 •MTBF = 1/ λ 70 19 MIL-HDBK-217 cancelled 80 19 90 19 t. 2 no 00 : F- -2 8 20 - 02 3 95 19
  • 4. Predicting the failure rate, MIL-HDBK-217 Electrolytic capacitors: λP = λbπ CV π Qπ E Failures / 10 hours 6 Microprocessors: λP = (C1π T + C2π E )π Qπ L Failures / 10 hours 6 • • • For the entire system: i =n 1 λSYSTEM = ∑ λP ,i ⇒ MTBFSystem = i =1 λSystem 4
  • 5. Necessary assumptions for prediction: •Constant failure rate •1 Toyota for 7.000 hours = 7.000 Toyota’s for 1 hour •A new car fails just as often as an old car that has run 300.000 km •System = sum of its components •No tolerance problems. No interface problems. •SW quality doesn’t matter! •1 line-of-code identical to 70.000 lines-of-code •Mechanical quality doesn’t matter! •However, you can use NPRD-95 (Non-electronic Parts Reliability Data), which is much less refined than MIL-HDBK-217 •All system manufacturers have identical production quality •Only 14 different environments (of which 11 are military) •The authors of MIL-HDBK-217 know the quality of your product. 5
  • 6. Basic belief for the use of MIL-HDBK-217 - And it’s many sisters: HRD-5, RDF-2000, Italtel, Telcordia/Bellcore … •Product reliability is inherent in the components. When a component fails, the cause should be found in the component itself. •This was more true in the 1960’ies when the failure pattern was caused by many electronic component defects, due to low production quality. •Today, this viewpoint is obsolete. Grundfos does not use this type of reliability prediction. 6
  • 7. ”To meet any reliability objective requires comprehensive knowledge of the interaction of failure mechanisms, failure modes, the mission profile, and the design of the product.” J1879, Handbook for robustness validation of semiconductor devices in automotive applications 7