DocumentCode :
3285906
Title :
TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model
Author :
Howells, Gareth ; Howlett, Bob ; McDonald-Maier, Klaus
Author_Institution :
Univ. of Kent, Canterbury
fYear :
2007
fDate :
5-8 Aug. 2007
Firstpage :
647
Lastpage :
651
Abstract :
The increasing availability of advanced computer equipment and sensory systems often results in large volumes of data, with subsequent difficulties in efficient analysis and real-time processing. The Tricoda initiative focuses on tools and techniques to aid in the automated analysis of large, complex systems and the data sets they generate. A novel general-purpose modelling system is employed based on the combination of a number of artificial intelligence based and conventional techniques, all integrated with a novel formal framework based on Constructive Type Theory. The tool is evaluated for the solution of a data analysis and condition monitoring case study focusing on an automotive application, specifically the automotive sector for engine control.
Keywords :
automotive engineering; condition monitoring; data analysis; internal combustion engines; neural nets; type theory; Tricoda; advanced computer equipment; artificial intelligence; automotive application; condition monitoring; constructive type theory; data analysis; engine control; neural network; real-time processing; sensory systems; Artificial intelligence; Automotive engineering; Availability; Condition monitoring; Data analysis; Industrial plants; Machinery; Neural networks; Sensor phenomena and characterization; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Adaptive Hardware and Systems, 2007. AHS 2007. Second NASA/ESA Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-7695-2866-3
Type :
conf
DOI :
10.1109/AHS.2007.107
Filename :
4291980
Link To Document :
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