Vibration monitoring of EDF rotating machinery using artificial neural networks
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Electricite de France, 78 - Chatou (France). Direction des Etudes et Recherches
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.
- Research Organization:
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG07-88ER12824
- OSTI ID:
- 10104402
- Report Number(s):
- CONF-9109110-14; ON: DE93003557
- Resource Relation:
- Conference: International conference on frontiers in innovative computing for the nuclear industry,Jackson, WY (United States),15-18 Sep 1991; Other Information: PBD: [1991]
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NUCLEAR POWER PLANTS
REACTOR MONITORING SYSTEMS
NEURAL NETWORKS
MECHANICAL VIBRATIONS
SYSTEM FAILURE ANALYSIS
REACTOR NOISE
ALGORITHMS
220900
990200
REACTOR SAFETY
MATHEMATICS AND COMPUTERS