Sensor Signal Analysis By Neural Networks For Surveillance In Nuclear Reactors

The application of neural networks as a tool for reactor diagnostics is examined here. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft [17] are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2-A) paradigm of neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques and is capable of distinguishing these signals apart and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data and provides an evaluation on the performance of ART 2-A and ART 2 for reactor signal analysis. The selection of ART 2 is due to its desired design principles such as unsupervised learning, stability-plasticity, search-direct access, and the match-reset tradeoffs. ART 2-A is selected for its speed. Two simulators are built. One is ART 2, and the other ART 2-A. The result is a success for both paradigms, and the study shows that ART 2-A is not only able to learn and distinguish the patterns from each other, its learning speed is also extremely fast despite the high-dimensional input spaces. © 1992 IEE

Diagnostics and Control of Natural Gas-Fired furnaces via Flame Image Analysis using Machine Vision & Artificial Intelligence Techniques

A new approach for the detection of real-time properties of flames is used in this project to develop improved diagnostics and controls for natural gas fired furnaces. The system utilizes video images along with advanced image analysis and artificial intelligence techniques to provide virtual sensors in a stand-alone expert shell environment. One of the sensors is a flame sensor encompassing a flame detector and a flame analyzer to provide combustion status. The flame detector can identify any burner that has not fired in a multi-burner furnace. Another sensor is a 3-D temperature profiler. One important aspect of combustion control is product quality. The 3-D temperature profiler of this on-line system is intended to provide a tool for a better temperature control in a furnace to improve product quality. In summary, this on-line diagnostic and control system offers great potential for improving furnace thermal efficiency, lowering NOx and carbon monoxide emissions, and improving product quality. The system is applicable in natural gas-fired furnaces in the glass industry and reheating furnaces used in steel and forging industries

Progress report no. 7

Statement of responsibility on title-page reads: editor: M.J. Driscoll; contributors: D.C. Aldrich, M.J. Driscoll, O.K. Kadiroglu, S. Keyvan, H.U.R. Khan, D.D. Lanning, R. Morton, J. Pasztor, T.J. Reckart, A.A. Salehi, J.I. Shin, A.T. Supple, D.J. Wargo, and S.S. WuIncludes bibliographical referencesProgress report; September 30, 1976U.S. Atomic Energy Commission contracts: E(11-1) 225