Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Alex Shenfield Modelling, Optimisation and Decision Support Using the Grid Rolls-Royce University Technology Centre in Control & Systems Engineering Department of Automatic Control & Systems Engineering The University of Sheffield, UK.

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Overview of Presentation 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project 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.

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project 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 Propose Solution AdaptJustify Criticize Evaluate Store

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project CBR Engine Architecture SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser) Description of situation Description of problem in that situation Description of how problem was addressed Results or outcome of addressing the problem in that way CASE Description of situation Description of problem in that situation Description of how problem was addressed Results or outcome of addressing the problem in that way CASE Description of situation Description of problem in that situation Description of how problem was addressed Results or outcome of addressing the problem in that way CASE Description of situation Description of problem in that situation Description of how problem was addressed Results or outcome of addressing the problem in that way CASE

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project CBR Engine Architecture SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser) Interface between application and data Reconfigurable

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project CBR Engine Architecture SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser) Contains CBR matching and ranking algorithms

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project CBR Engine Architecture SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser) Processes calls to the CBR service Returns results from the CBR service

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project CBR Engine Architecture SQL Database Database Manager CBR Engine (API) Service Interface Grid/Web Service Client (Web Browser)

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Genetic Algorithms 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project A Simple Genetic Algorithm Generate Initial Population Fitness Evaluation Finished? Selection Recombination Mutation Yes No Genetic Algorithm : generate next generation of solutions for evaluation

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Multi-Objective Optimisation Many real-world engineering design problems often involve solving multiple (often conflicting) objectives An ideal multi-objective optimisation procedure is: 1)Find multiple Pareto optimal solutions for the objectives

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Multi-Objective Optimisation Many real-world engineering design problems often involve solving multiple (often conflicting) objectives An ideal multi-objective optimisation procedure is: 1)Find multiple Pareto optimal solutions for the objectives 2)Choose one of the trade- off solutions using higher level information

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Integrated Logistic Support Strategy Optimisation 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 THE GRID !

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project MOGA-G Architecture

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project DAME Use Case

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project DAME Use Case In the future: MEAROS MODEL Failure Rate Data learnt from DAME

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Security 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Conclusions 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

Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science DAME Project Thanks! The authors gratefully acknowledge the financial support of the EPSRC and the valuable input from engineers at Rolls-Royce and Data Systems & Solutions