Rolls-Royce University Technology Centre in Control and Systems Engineering X. Ren, M. Ong, G. Allan, V. Kadirkamanathan, H. A. Thompson and P. J. Fleming Rolls-Royce UTC in Control and Systems Engineering Department of Automatic Control and Systems Engineering University of Sheffield UK Integrated Fault Diagnostics on the Grid NCAF Sunderland Meeting 09/06/2004

Rolls-Royce University Technology Centre in Control and Systems Engineering Outline Model-based fault diagnosis Service-oriented architecture for Integration DAME project An engine simulation Grid service Case-based reasoning for fault analysis Security Issues Conclusions and future work

Rolls-Royce University Technology Centre in Control and Systems Engineering Model-based Fault Diagnosis Basic idea: the measurements from dissimilar sensors are functionally related. Any violation of these relationships indicates the occurrence of faults.

Rolls-Royce University Technology Centre in Control and Systems Engineering Model-based Fault Detection and Isolation (FDI) Promptness of fault detection Sensitivity to incipient faults False alarm rate and missed fault detection Incorrect fault identification Observer approach Parameter estimation Statistical approach Qualitative method Different Approaches: Evaluation Criteria:

Rolls-Royce University Technology Centre in Control and Systems Engineering How to Improve? Adaptive fault-detective threshold Likelihood Ratio Test Robust approaches Soft computing approaches Combination of quantitative and qualitative methods

Rolls-Royce University Technology Centre in Control and Systems Engineering Necessity of Integration Different FDI algorithms have been developed Each approach has its own focus; no “silver bullet” Integration of several FDI schemes will provide better decision support for fault diagnosis and condition-based maintenance HPC power is often a burden for implementations of different algorithms Large data storage capability are needed for online/offline monitoring and fault analysis Web Services and service-oriented architecture (SOA) for integration

Rolls-Royce University Technology Centre in Control and Systems Engineering Service-oriented architecture (SOA) An architecture made up of components and interconnections Designed to stress interoperability and location transparency Examples include DCOM, CORBA and Web services Web services are the most promising techniques for SOA Implementation on the Internet Structure of Web services Integration of FDI Tools based on SOA Different FDI approaches can be wrapped and provided as Web services on the Internet

Rolls-Royce University Technology Centre in Control and Systems Engineering Integration of FDI Tools on the Grid Grid: a distributed computing infrastructure that provides dependable, consistent, pervasive and inexpensive access to high-end computational capabilities “Coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organisations” -- Ian Foster The Grid provides the potential HPC power and large data storage capability for implementation of different algorithms Service-Oriented Architecture and the Open Grid Services Architecture (OGSA) for the integration

Rolls-Royce University Technology Centre in Control and Systems Engineering DAME Project Distributed Aircraft Maintenance Environment An UK e-Science Pilot Project demonstrating the use of the Grid to implement a distributed decision support system for maintenance Web Link: Academic and Industrial Partners:

Rolls-Royce University Technology Centre in Control and Systems Engineering DAME Architectural Overview QUOTE Data-Mining Signal Processing Case Based Reasoning Novel Data R a w E n g i n e D a t a Vibration Shaft Speed Fuel Flow S e r v i c e D a t a P a r t s D a t a Grid Services Management Modelling/ Simulation O p e r a t i o n a l D a t a The Grid... AURA-G DAME Grid Portal

Rolls-Royce University Technology Centre in Control and Systems Engineering Sheffield Project Goals Simulation, visualisation and decision support environment for engine fault diagnosis and maintenance Integration of modelling, estimation and analysis tools for engine fault diagnostics

Rolls-Royce University Technology Centre in Control and Systems Engineering Engine Simulation as a Grid Service An Grid service for engine simulation based on the Rolls-Royce engine performance model Based on the Globus Toolkit 3 (GT3) and the Open Grid Service Infrastructure (OGSI) for distributed systems integration The engine simulation Grid service can be invoked simultaneously in different “Virtual Organisations” for different applications

Rolls-Royce University Technology Centre in Control and Systems Engineering Features of the Engine Simulation Grid Service The simulation: can simulate for different flight operational conditions and requirements, e.g. Idle, Take-off, Climb, Cruise takes different variables, including Net Thrust, Turbine Temperature, Spool Speed and Fuel Flow, as control input As Grid services: GSI enabled secure engine performance simulation via Internet a bunch of simulation instances generated by the factory service ; different engine simulation can be performed simultaneously Only lightweight computers or PDAs are needed to perform the engine simulation, while still benefit from high-performance/high- throughput computing power provided by the Grid

Rolls-Royce University Technology Centre in Control and Systems Engineering Access the Grid Service through Web Browsers A Web server for accessing the Engine Simulation Grid Service has been developed Use JSP to invoke the simulation service Use Web browser to view the engine performance data and the simulation result Two way SSL authentication via server and client certificates

Rolls-Royce University Technology Centre in Control and Systems Engineering Applications A simulation-based fault detection can be performed Experienced maintenance engineers can use this service to find the problem via on-line simulation Can be used to confirm the fault assumption Can be invoked by different fault diagnostic algorithms Feature Detection and Event Generation Raw Measurement Observation Processing (Event History Generation) Combination (CBR or Other) Fault Diagnosis, Maintenance Advice Engine Performance Model

Rolls-Royce University Technology Centre in Control and Systems Engineering CBR for Fault Analysis Emulates the diagnostic skill of an experienced maintenance engineer Provides the user with “best practice” advice when confronted with a set of fault symptoms Provides a confidence measure for each suggested solution Facilitates the processing of logistic data, current and historical fault data to isolate engine problems

Rolls-Royce University Technology Centre in Control and Systems Engineering 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 Case-Based Reasoning “Reasoning by remembering, reasoning is remembered.”

Rolls-Royce University Technology Centre in Control and Systems Engineering Cases link together the knowledge about: Information concerning the fault Response to the fault Effects of those responses 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” Cases Cases represent: Engine information Individual engine fault events Explicit fault symptoms and patterns Maintenance actions

Rolls-Royce University Technology Centre in Control and Systems Engineering CBR Maintenance Advisor CBR Maintenance Advisor as a Grid Service: Based on Globus 3 Toolkit Access to knowledge gained from previous fault diagnosis Obtain maintenance advice in form of appropriate maintenance action

Rolls-Royce University Technology Centre in Control and Systems Engineering Decision Support for DAME

Rolls-Royce University Technology Centre in Control and Systems Engineering Security Issues Challenges: Eavesdropping Tampering Impersonation Current solutions: Symmetric-Key encryption Public-Key encryption Digital Signatures using PKI Certificate-Based Authentication The secure sensitive engine model and user data need to be protected! The Engine Simulation Grid Service uses GSI to secure the model and user data SSL is used to secure the Web and database server

Rolls-Royce University Technology Centre in Control and Systems Engineering GSI and SSL Implementation Grid Security Infrastructure(GSI) the security functionality implemented by Globus Toolkit Secure Socket Layer(SSL) a technology for secure connection between Web browsers and servers. Both use certificates for authentication SSL uses public key encryption to generate shared secrets; then performs the symmetric encryption GSI provides the Transport Layer Security and Message Level Security Transport Layer Security: a Grid version of SSL Message Level Security: based on WS-Security, XML Encryption and XML Signature standards

Rolls-Royce University Technology Centre in Control and Systems Engineering Conclusions and Future Work Importance of integration for fault diagnostics SOA implemented on the Grid for FDI integration The Engine Simulation Grid Service CBR for fault analysis and maintenance Performance-based engine fault diagnosis More Grid services for engine fault diagnosis Knowledge-based workflow advising