Technology Lecture on Long Term Prediction Approaches based on Connexionist Systems for Prognostics
Date: 11 Oct 2011 - 11 Oct 2011
Venue: SIMTech Training Room, Level 3
Data-driven approaches are increasingly applied to machine prognostics. More precisely, connexionist systems like neural networks and neuro-fuzzy systems benefit from a growing interest. Indeed, their approximation capability makes them as powerful candidates to achieve the prediction step of prognostics, Nevertheless, prognostics implies to be able to perform multistep ahead predictions whereas many works focus on short term predictions. This lecture provides useful insights into the connexionist-systems-based approaches to ensure long term predictions for prognostics. Participants will gain a better understanding of the experiments on various benchmarking and best practices for industrial applications. This lecture is co-organised by the IEEE Industrial Electronics Society Singapore Chapter.
About the Speaker
Dr Rafael Gouriveau graduated with an Engineering degree from National Engineering School of Tarbes (ENIT) in 1999. He then obtained his MS (2000) and PhD in Industrial Systems in 2003, both from the Toulouse National Polytechnic Institute (INPT). While pursuing his PhD, he worked in the field of risk management and dependability analysis. In September 2005, he joined the national high school of mechanics and microtechnology in Besançon, France (Ecole Nationale Supérieure de Mécanique et des Microtechniques, ENSMM) as Associate Professor in the field of production, maintenance, manufacturing, and informatics domains. His current research interests include the development of industrial prognostics systems via neuro-fuzzy methods and the investigation of reliability modelling by using possibility theory.
10.00am Presentations by Dr Rafael Gouriveau
11.00am Refreshment & Networking
Who Should Attend
Research academic staff, students, senior management, R&D managers and engineers.
Registration for this lecture is free of charge. To reserve a place, please register online.
For technical enquiries, please contact Dr Li Xiang, Email: xli@SIMTech.a-star.edu.sg
; Tel: 6793 8264
For registration & general enquiries, please contact Alice Koh, Email: email@example.com
; Tel: 6793 8249