A prototype of a Signal Monitoring System (SMS) utilizing artificial neural networks is developed in this work. The prototype system is unique in: 1) its utilization of state‐of‐the‐art technology in pattern recognition such as the Adaptive Resonance Theory family of neural networks, and 2) the integration of neural network results of pattern recognition and fault identification databases.
The system is developed in an X‐windows environment that offers an excellent Graphical User Interface (GUI). Motif software is used to build the GUI. The system is user‐friendly, menu‐driven, and allows the user to select signals and paradigms of interest. The system provides the status or condition of the signals tested as either normal or faulty. In the case of faulty status, SMS, through an integrated database, identifies the fault and indicates the progress of the fault relative to the normal condition as well as relative to the previous tests.
Nuclear reactor signals from an Experimental Breeder Reactor are analyzed to closely represent actual reactor operational data. The signals are both measured signals collected by a Data Acquisition System as well as simulated signals.
OBJECTIVESA new approach for detection of real-time properties of flames was used in this project to develop sensors to improve diagnostics and controls for natural gas fired furnaces. Camera images along with advanced image analysis and artificial intelligence techniques were used to provide high speed virtual sensors suitable for integration with the plant control system. The output of these sensors provides guidance for balancing air/fuel ratios. Identifying and correcting fuel rich burners would result in improved fuel efficiency. It is anticipated that this on-line diagnostic and control system will offer great potential for improving furnace thermal efficiency, lowering NO x and carbon monoxide emissions, and improving product quality.This project addresses the need for improved diagnostics and burner-balancing control of natural gas fired glass furnaces in order to improve fuel efficiency and reduce emissions. The project involved two Phases. Phase I was a feasibility study with one year duration and was completed in This report starts with a summary of accomplishments during phase I (February 2000(February -2001 followed by a summary of accomplishments during each year of phase II (February 2001-December 2004.
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SUMMARY OF ACCOMPLISHMENTS DURING PHASE IPhase I activities included: 1) Camera and spectrometer equipment selection and acquisition, 2) Data acquisition at a pilot-scale glass furnace in Rolla, Missouri, and at a research combustion facility in Pennsylvania, 3) spectroscopic data analysis and temperature calculation based on black body radiation model, and 4) Flame image analysis including image processing and feature extraction and classification by two ART neural network algorithms. At the end of Phase I a flame classification simulation game was developed as a marketing tool.
Highlight of Phase I activities:• Equipment Acquisition:• Periscope, camera, Spectrometer, computer, and laptopSeveral experiments were performed for collecting data using both spectrometer and camera at the following two laboratory scale furnaces. A simulation game module was also developed for demonstration of the concept. Figure 1 shows the cover page of this module which can be played on the web using JAVA Media Frame Player and is currently located at: http://www.missouri.edu/~keyvans/public_html/demo/cover.html
Pilot Scale Glass
Pilot Scale Glass FurnaceThis pilot scale furnace was a 0.16 Million Btu/hr glass furnace designed to carry out simulation and parametric studies of industrial glass tanks. The burner of this furnace was of the diffusion flame type, typically used in glass furnace applications. The furnace was controlled using a Labview hardware and software control system. Figure 2 shows the equipment set-up at this furnace. Figure 3 shows the computer screen of the furnace Labview software.
Combustion FacilityCombustion experimentations were conducted using a 2 Million Btu/hr combustion facility (see Figure 4) at the Pennsylvania State University in September and October 2000. The objective was to per...
kbstruct-The application of neural networks as a tool for toring. Signals utilized in a wear-out monitoring system 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.
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