DocumentCode :
2272219
Title :
Artificial neural network models for predicting degradation trends in system components and sensors
Author :
Naghedolfeizi, Masoud ; Arora, Sanjeev
Author_Institution :
Dept. of Math. & Comput. Sci., Fort Valley State Univ., GA, USA
fYear :
2003
fDate :
22-25 Sept. 2003
Firstpage :
647
Lastpage :
651
Abstract :
A prediction model based-on artificial neural network technology was developed for trend forecasting of a given degradation process in a system component. The model utilizes the engineering analysis of the degradation process under study with the analysis of process field data and information to predict future trend in the degradation. The neural network prediction models were applied to simulated degradation data of a typical system component. The prediction results showed that the neural network models were capable of recognizing the correct future degradation trends in data even with a limited amount of input data. In addition, the models were able to capture the dynamics and nonlinearities associated with the degradation process data.
Keywords :
forecasting theory; neural nets; reliability; artificial neural network models; degradation process data; degradation process trend forecasting; degradation trend prediction model; system component degradation; system sensor degradation; Artificial neural networks; Biological neural networks; Biological system modeling; Degradation; Information analysis; Inspection; Intelligent networks; Mathematics; Predictive models; Sensor systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
AUTOTESTCON 2003. IEEE Systems Readiness Technology Conference. Proceedings
ISSN :
1080-7725
Print_ISBN :
0-7803-7837-7
Type :
conf
DOI :
10.1109/AUTEST.2003.1243645
Filename :
1243645
Link To Document :
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