Alex Shenfield a.shenfield@sheffield.ac.uk

1. Modelling, Optimisation and Decision Support Using the Grid Alex Shenfield a.shenfield@sheffield.ac.uk Rolls-Royce University Technology Centre in Control & Systems Engineering Department of Automatic Control & Systems Engineering The University of Sheffield, UK.

2. Introduction : UK e-Science DAME project Motivation for DAME DAME Grid-Based Diagnostic System Case Based Reasoning Model Based Fault Detection and Isolation Approaches Genetic Algorithms for Many-Objective Optimisation Use Case Conclusions Overview of Presentation

3. Introduction to DAME • £3.2M UK e-Science Pilot Project • Develop, and promote understanding of : • Grid middleware and application/services layer integration • Real-time issues in Grid Computing • Dependability Issues • Provide a “Proof of Concept” demonstrator for the Rolls-Royce Engine Diagnostic problem

4. Project Partners • Four UK Universities : • University of York • Computer Science Department • University of Sheffield • Automatic Control and Systems Engineering Department • University of Leeds • School of Computing • School of Mechanical Engineering • University of Oxford • Engineering Science Department • Industrial Partners : • Rolls-Royce Aeroengines • Data Systems and Solutions • Cybula Ltd.

5. Motivation for DAME • Increasing amounts of engine data being collected • New engine monitoring units record up to 1 Gbyte of data per flight • Rolls-Royce currently has over 50,000 engines in service with total operations of around 10M flying hours per month • In the future, terabytes of data will be transmitted every day for analysis • Key Objectives • Reduce delays • Reduce cost of ownership for the aircraft

6. “Reasoning by remembering, reasoning is remembered.” Case-Based Reasoning • CBR is a mature, low-risk subfield of AI • Primary knowledge source • A memory of stored cases recording specific prior episodes • Not generalised rules • New solutions generated by adapting relevant cases from memory to suit new situations Retrieve ProposeSolution Adapt Justify Criticize Evaluate Store

7. CBR Maintenance Advisor • Integrates fault information and knowledge gained from the fault diagnosis process • Emulate the diagnostic skill of an experienced maintenance engineer • Advises maintenance personnel on appropriate maintenance action • Deployed as a Grid Service

8. SQL Database “CASE” “CASE” “CASE” “CASE” Database Manager • Description of situation • Description of situation • Description of situation • Description of situation CBR Engine (API) • Description of problem • in that situation • Description of problem • in that situation • Description of problem • in that situation • Description of problem • in that situation • Description of how • problem was addressed • Description of how • problem was addressed • Description of how • problem was addressed • Description of how • problem was addressed Service Interface • Results or outcome of • addressing the problem • in that way • Results or outcome of • addressing the problem • in that way • Results or outcome of • addressing the problem • in that way • Results or outcome of • addressing the problem • in that way Grid/Web Service Client (Web Browser) CBR Engine Architecture

9. SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser) CBR Engine Architecture • Interface between application and data • Reconfigurable

10. SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser) CBR Engine Architecture • Contains CBR matching and ranking algorithms

11. SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser) CBR Engine Architecture • Processes calls to the CBR service • Returns results from the CBR service

12. SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser) CBR Engine Architecture

13. Model Based FDI • Data from the real engine is compared against data from the ideal model • The residuals then need to be analysed to work out the state of the engine • This can be used to track changes in engine parameters which may indicate impending faults

14. Engine Modelling and Simulation Service • Based on the Rolls-Royce Trent 500 engine model • Deployed as a service on the Grid • Accessible via web browser on the internet • Grid factories enable parallel execution of multiple simulation instances

15. Genetic Algorithms (GAs) are global search algorithms based on the mechanics of natural selection GAs are robust search methods: Can escape local optima Can deal with ‘noisy’ or ill-defined evaluation functions Some features of GAs are: GAs search a population of points GAs use objective function pay-off information GAs are stochastic Genetic Algorithms

16. Generate Initial Population Fitness Evaluation Yes Finished? No Genetic Algorithm : generate next generation of solutions for evaluation Selection Recombination Mutation A Simple Genetic Algorithm

17. Many real-world engineering design problems often involve solving multiple (often conflicting) objectives Multi-Objective Optimisation • An ideal multi-objective optimisation procedure is: • Find multiple Pareto optimal solutions for the objectives

18. Many real-world engineering design problems often involve solving multiple (often conflicting) objectives Multi-Objective Optimisation • An ideal multi-objective optimisation procedure is: • Find multiple Pareto optimal solutions for the objectives • Choose one of the trade-off solutions using higher level information

19. MEAROS Optimisation: Removal of aircraft engines is expensive By using GAs to optimise soft lives of engine components in the MEAROS simulation we can develop ‘optimal’ preventative maintenance strategies Issues: MEAROS is a complex stochastic simulation, therefore it has to be run multiple times for each candidate solution to reduce the effect of random variations This requires a lot of computing power Integrated Logistic Support Strategy Optimisation THE GRID !

20. MOGA-G Architecture

21. DAME Use Case

22. DAME Use Case • In the future: Failure Rate Data learnt from DAME MEAROS MODEL

23. The Decision Support System will contain sensitive data, therefore access must be restricted i.e. Knowledge Base and Engine Model contain information on the design characteristics and operating parameters of the engine Security implemented using Globus Toolkit to provide: Public Key Encryption X509 certificates SSL communications Security

24. Move from local diagnostic support to centralised, distributed diagnostic support Integration of Model-Based FDI, CBR and Optimisation Business Benefits : Reduction in unscheduled maintenance Reduction in aircraft downtime Conclusions

25. The authors gratefully acknowledge the financial support of the EPSRC and the valuable input from engineers at Rolls-Royce and Data Systems & Solutions Thanks!