Vibration monitoring of EDF rotating machinery using artificial neural networks
Abstract
Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring andmore »
- Authors:
-
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Electricite de France, 78 - Chatou (France). Direction des Etudes et Recherches
- Publication Date:
- Research Org.:
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Sponsoring Org.:
- USDOE; USDOE, Washington, DC (United States)
- OSTI Identifier:
- 6862318
- Report Number(s):
- CONF-9109110-14
ON: DE93003557
- DOE Contract Number:
- FG07-88ER12824
- Resource Type:
- Conference
- Resource Relation:
- Conference: International conference on frontiers in innovative computing for the nuclear industry, Jackson, WY (United States), 15-18 Sep 1991
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 22 GENERAL STUDIES OF NUCLEAR REACTORS; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; NEURAL NETWORKS; NUCLEAR POWER PLANTS; REACTOR MONITORING SYSTEMS; ALGORITHMS; MECHANICAL VIBRATIONS; REACTOR NOISE; SYSTEM FAILURE ANALYSIS; MATHEMATICAL LOGIC; NUCLEAR FACILITIES; POWER PLANTS; SYSTEMS ANALYSIS; THERMAL POWER PLANTS; 220900* - Nuclear Reactor Technology- Reactor Safety; 990200 - Mathematics & Computers
Citation Formats
Alguindigue, I E, Loskiewicz-Buczak, A, Uhrig, R E, Hamon, L, and Lefevre, F. Vibration monitoring of EDF rotating machinery using artificial neural networks. United States: N. p., 1991.
Web.
Alguindigue, I E, Loskiewicz-Buczak, A, Uhrig, R E, Hamon, L, & Lefevre, F. Vibration monitoring of EDF rotating machinery using artificial neural networks. United States.
Alguindigue, I E, Loskiewicz-Buczak, A, Uhrig, R E, Hamon, L, and Lefevre, F. 1991.
"Vibration monitoring of EDF rotating machinery using artificial neural networks". United States. https://www.osti.gov/servlets/purl/6862318.
@article{osti_6862318,
title = {Vibration monitoring of EDF rotating machinery using artificial neural networks},
author = {Alguindigue, I E and Loskiewicz-Buczak, A and Uhrig, R E and Hamon, L and Lefevre, F},
abstractNote = {Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.},
doi = {},
url = {https://www.osti.gov/biblio/6862318},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 01 00:00:00 EST 1991},
month = {Tue Jan 01 00:00:00 EST 1991}
}