An Enterprise Approach to Engine Test Analysis: Requirements for Implementation

54 %
46 %
Information about An Enterprise Approach to Engine Test Analysis: Requirements for...

Published on October 27, 2016

Author: SGSgroup

Source: slideshare.net

1. SGS TRANSPORTATION AN ENTERPRISE APPROACH TO ENGINE TEST ANALYSIS: REQUIREMENTS TO IMPLEMENTATION

2. 2 Nº1 WORLD LEADER 85,000 EMPLOYEES 1,800+ OFFICES AND LABORATORIES 12 GLOBAL INDUSTRIES GLOBAL SERVICE LOCAL EXPERTISE YOU KNOW: ENGINE R&D INVOLVES INNOVATION: DYNAMIC CHANGE EVOLUTION: BUILDING ON THE PAST OPTIMIZATION: TUNING TO NEEDS CONFIRMATION: DEMONSTRATING COMPLIANCE

3. 3 BRUCE THOMASON Director of Technology for SGS, Transportation A career built on systems engineering, test technology, and development and testing of complex systems in aerospace, automotive, and power generation.

4. 4 R&D TEST ANALYTICS COMPLICATIONS  Product range: Architecture, Size and Fuels  Experiment types: Transient, Steady State and Extreme Condition  Measurement types: Time-Based, Spatial and Batch Instruments  Analysis methods: Standard and Proprietary

5. 5 ADDED FACTORS INTERFERE  Naming conventions  Departmental  Business unit  Industry  Regulatory  Units of Measure  System  Conversions  Naming  Change in analysis methods  Development  Validation  Distribution

6. 6 INTERNAL OPERATING CHALLENGES  Not using:  Vetted engineering practices  In-house expertise  Not knowing:  Measurement uncertainty  Best practices  Not tracking or storing:  Test details  Test setups  Test conditions Everyone is fighting fires!

7. 7 SPREADSHEET SWAMP  Spreadsheets ‘spread’ like a healthy mold  Maintenance not performed  Analytics ill-suited to spreadsheets are crammed in anyway

8. 8 THE UNFORTUNATE RESULTS  A less rich set of information  A less correct set of information  A short half-life for experimental results  An inconsistent basis for understanding results from:  Test to test  Product to product  Person to person  Over time

9. 9 IMPROVED INFORMATION OPPORTUNITY: IMPROVED TEST DERIVED INFORMATION BREADTH: DEEPER, BROADER QUALITY: BETTER EFFICIENCY: MORE EASILY DERIVED RESULTS: USEFUL INFORMATION

10. 10 WHY THIS FOCUS? Enterprise Process Element Example Enterprise Tools Test automation SGS CyFlex®, National Instruments LabVIEW® Calibration and equipment management Fluke MET/TEAM® Raw data storage and retrieval ASAM Open Data Services Test Results Analysis SGS Mach Analytics™ Scientific Visualization and Reporting Too many to mention… This space was under-served

11. 11 EXPERIMENTATION RESULTS ANALYTICS  For this presentation:  First principal methods  To transform raw measurements  Into useful engineering information  But, with great enterprise focus:  High test volume  Large product and test diversity  Long term, big picture features Anyone can put F=ma in a spreadsheet, but its hard to put an engine in one!

12. 12 REQUIREMENT AREAS  Enterprise focused areas to address:  Research teams  Engineering discipline functions  Product development teams and processes  Enterprise goals  IT infrastructure and process  Lifecycle considerations  Cross-organization factors Its not just about the math!

13. 13 RESEARCH TEAM Driven by innovation, speed, fail fast and forward, new components and arrangements. No pain, no gain! Area of need in analytics Examples Adaptability in product-to-be Product configuration change, component change Adaptability in methods New experiment types, new instrumentation, new analysis Rapid pace and changing directions Try and use or throw away What-if-ing Extrapolation, overriding values, comparing to models Down stream knowledge flow The ability capture and convey successful results and methods

14. 14 ENGINEERING DISCIPLINE LEADERS Driven by stewardship of our knowledge area, we sweat the details. Area of need in analytics Examples Development Creation of new or improved methodologies Validation Vetting methods during and after development Quality assurance On-going correctness, repeatability, release-for-use management Knowledge transfer Efficiently supporting teams using developed methods.

15. 15 PRODUCT DEVELOPMENT PRIORITIES “Get the product developed and validated, yesterday.” “Our deadline is when?” Area of need in analytics Examples Efficiency High data volumes, rapid cycles Ease of access and use Don’t slow me down, don’t make me learn too much Quality assurance Leveraging vetted and standard methods Accountability Having traceable results

16. 16 INFORMATION TECHNOLOGY GOALS “We need to keep this thing running for decades without slowing down the engineers.” Area of need in analytics Examples Supportability Installed platforms: servers, web applications, test systems, SAAS/PAAS Transparency Available and knowledgeable help Standards adherence Industry and internal Control Centralization, access control, knowledge Predictability Performance, reliability, resource use Security Need to know, hacker threats

17. 17 ENTERPRISE GOALS “Let’s keep this money pump humming along, but make it better as we go.” Area of need in analytics Examples Definition and Standardization Of process, methods, and actual behavior… easier to count on and improve a known foundation. Clarity of ownership Process and content owners … know where to turn for help and improvement. Innovation Create and understand product differentiation and market advantage Commercial sensitivity Value creation, correctness, avoidance of rework/warranty Security Need-to-know basis for staff, suppliers, customers, regulatory agents

18. 18 INTER- AND INTRA-ORGANIZATION ISSUES Area of need in analytics Examples Naming convention differences Departmental, OEM1 vs. OEM2 vs. regulatory Product and component type differences and change Engines (themselves with highly variable architectures) vs. generator sets vs. vehicles Segregation & security Industry or regulatory standard vs. OEM proprietary Data store variability Relational database, ODS, flat file, live test automation system Information sink variability End user/tool, batch process, live test automation system “Our stuff has to work for everybody, everyday.”

19. 19 ANALYSIS METHODS LIFECYCLE CONSIDERATIONS Area of need in analytics Examples Support for life stages of methods From what-if concepts “sand boxing” to production use to “retired-but-keep- around-as-reference” Driven by Product change, regulatory change, evolving instrumentation and experimentation methods Involving libraries, systems and interfaces That are added, evolve, replaced, or made obsolete “The world changes …… got to keep up!”

20. 20 THE PUNCHLINE  The preceding is possible  It’s been done!  SGS Mach Engine Analytics Software  The preceding is possible  Approach  Outcomes  Lessons learned

21. 21 MACH APPROACH  A base set of “components” is available and extensible  Components are backed by component- specific analysis methods  A “unit under test topology” defines the test article as component connections: typically fluid or energy flows  Test measurements associated with specific states of components or connections  Mach combines available information to calculate other derivable results Environmental Conditions ▪ Altitude ▪ Temperature ▪ Humidity ▪ Grade ▪ Air Handling Systems ▪ Single stage turbo ▪ Sequential turbo ▪ Intercooler ▪ Intake throttle ▪ Exhaust Gas Recirculation ▪ Low pressure EGR ▪ High pressure EGR ▪ Diesel Fuel Injection Systems ▪ Fuel supply/return ▪ Unit Injector ▪ Pump-line-nozzle ▪ Common Rail ▪ Camshaft and Valvetrain ▪ Lifters ▪ Variable valve actuation ▪ Synchronization ▪ Catalysts & Filters ▪ DOC ▪ DPF ▪ SCR ▪ LNT ▪ ASC ▪ TWC ▪ Exhaust Sensors ▪ Wide band Lambda ▪ Narrow band Lambda ▪ NOx sensor ▪ NH3 sensor ▪ Soot sensor

22. 22 CONSTRUCTS AND SAMPLES OF MACH ANALYTICS™

23. 23 EXAMPLE

24. 24 EXAMPLE

25. 25 LESSONS LEARNED  A broad and long term view tends to benefit from:  Domain experts  Modular architecture  Well-defined interfaces  Pluggable software modules  Built-in quality assurance  Domain-specific languages

26. 26 BIG DATA APPLICATIONS IN THE DYNO LAB: MACHINE LEARNING  Dynamometer lab data are used to create pattern recognition models for good and poor engine operation • Extreme environmental conditions • Sensitivity outside of OEM installation limits • Imposed faults/malfunction/abuse • Training using dyno lab measurements and known observed conditions  Analytics are then applied to large volumes of test data using cluster computing to discover similar poor operating conditions  Benefits include uncovering the scope of the problem and gaining insights for product improvement Training Data Good Operation Training Data Poor Operation Feature Extraction Model Training Model Validation Dyno Testing DoE Classical Statistics Model Analytics to Discover Problems in Populations Wide Environmental Test Space Extreme Regional Climate Data Example: Engine Sensitivity To Extreme Environmental Conditions

27. 27 A CLOSING CHALLENGE  If your organization is stuck in a spreadsheet swamp, re-think the possible  We’ve done it before, we can do it again  Faster  Cheaper  Better

28. 28 WHY SGS? LEVERAGING THE RIGHT TECHNOLOGY THINKING GLOBALLY, ACTING LOCALLY SERVICES AND SOLUTIONS ACROSS INDUSTRIES COMPLETE SUPPLY CHAIN SUPPORT STRONG MANAGEMENT TEAM AND LONGEVITY HIGH PRIORITY ON CUSTOMER SERVICE AND SATISFACTION CORPORATE CULTURE OF QUALITY AND SAFETY SYNERGISTIC & STRATEGIC CLIENT PARTNERSHIPS TRUSTEDFORIMPARTIALITY:INSPECTION,TESTING, VERIFICATIONANDCERTIFICATION INDUSTRYCOMMITMENTTHROUGHCONTINUED INVESTMENTANDACQUISITION INDEPENDENTLY STRONG, TOGETHER STRENGTHENED FOR GROWTH

29. WWW.SGS.COM ©SGSGroupManagementSA–2016–Allrightsreserved-SGSisaregisteredtrademarkofSGSGroupManagementSA

Add a comment