DocumentCode :
3662549
Title :
Degradation processes modelled with Dynamic Bayesian Networks
Author :
Anselm Lorenzoni;Michael Kempf
Author_Institution :
Department of Sustainable Production and Quality Management, Fraunhofer Institute for Manufacturing Engineering and Automation (IPA), 70569 Stuttgart, Germany
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
1694
Lastpage :
1699
Abstract :
In this paper a generic degradation model based on Dynamic Bayesian Networks (DBN) which predicts the condition of technical systems is presented. Besides handling bi-directional reasoning, a major benefit of using DBNs is its capability to adequately model stochastic processes. We assume that the behavior of the degradation can be represented as a P-F-curve (also called degradation or life curve). The model developed is able to combine information from condition monitoring systems, expert knowledge and any kind of observations like sensor data or notifications by the machine operator. Thus it is possible to even take the environment and stress into account under which the component or system is operating. Thus it is possible to detect potential failures at an early stage and initiate appropriate remedy and repair strategies.
Keywords :
"Degradation","Bayes methods","Maintenance engineering","Markov processes","Stress","Cloning"
Publisher :
ieee
Conference_Titel :
Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on
ISSN :
1935-4576
Electronic_ISBN :
2378-363X
Type :
conf
DOI :
10.1109/INDIN.2015.7281989
Filename :
7281989
Link To Document :
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