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
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