S4: Diagnostic reasoning
Model based reasoning to support diagnostics in complex systems
As systems become increasingly complex, diagnosing system failures and performance issues becomes a true challenge for engineers. Too often, solving problems requires extensive involvement of multiple R&D experts. Diagnostic reasoning gives engineers the tools they need to make decisions about system behaviour and performance without having to know all the intrinsic system details. In addition to presenting a state-of-the-art overview and a look into the future, this session presents industrial cases in which data, modelling, and reasoning are brought together to solve diagnostic challenges.
Moderation: Emile van Gerwen, ESI (TNO)
In his introduction Hans Onvlee will present a state-of-the-art overview on diagnostic reasoning and give a look into the future.
Diagnosing timing bottlenecks in large-scale component-based software
We introduce a new measurement-based approach to diagnose timing bottlenecks of existing large-scale component-based software. The approach is based on Timed Message Sequence Charts (TMSCs) that
capture the run-time execution of component-based software systems in a concise and intuitive way
are amenable to formal timing analysis, and
are automatically inferred from execution traces. We demonstrate the effectiveness of our approach by automatically computing critical paths in the software of an ASML lithography scanner to identify timing bottlenecks.
AI for Manufacturing
Artificial Intelligence applications in manufacturing range from improving reliability of equipment to improving operational effectiveness and providing quality insights. Using a simple framework this talk will discuss some practical examples of machine learning to predict failures and optimize equipment maintenance and process effectiveness. It will be shown that for a large class of algorithms explainability and transparency of machine learning based recommendations is an essential part of the solution. A discussion of a knowledge representation application will show how diagnostic reasoning can be assisted by artificial intelligence.