Extracting a Whisper from the DIN: A Bayesian-Inductive Approach to Learning an Anticipatory Model of Cavitation

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For several reasons, Bayesian parameter estimation is superior to other methods for inductively learning a model for an anticipatory system. Since it exploits prior knowledge, the analysis begins from a more advantageous starting point than other methods. Also, since "nuisance parameters" can be removed from the Bayesian analysis, the description of the model need not be as complete as is necessary for such methods as matched filtering. In the limit of perfectly random noise and a perfect description of the model, the signal-to-noise ratio improves as the square root of the number of samples in the data. Even with the ... continued below

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7 pages

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Kercel, S.W. November 7, 1999.

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For several reasons, Bayesian parameter estimation is superior to other methods for inductively learning a model for an anticipatory system. Since it exploits prior knowledge, the analysis begins from a more advantageous starting point than other methods. Also, since "nuisance parameters" can be removed from the Bayesian analysis, the description of the model need not be as complete as is necessary for such methods as matched filtering. In the limit of perfectly random noise and a perfect description of the model, the signal-to-noise ratio improves as the square root of the number of samples in the data. Even with the imperfections of real-world data, Bayesian methods approach this ideal limit of performance more closely than other methods. These capabilities provide a strategy for addressing a major unsolved problem in pump operation: the identification of precursors of cavitation. Cavitation causes immediate degradation of pump performance and ultimate destruction of the pump. However, the most efficient point to operate a pump is just below the threshold of cavitation. It might be hoped that a straightforward method to minimize pump cavitation damage would be to simply adjust the operating point until the inception of cavitation is detected and then to slightly readjust the operating point to let the cavitation vanish. However, due to the continuously evolving state of the fluid moving through the pump, the threshold of cavitation tends to wander. What is needed is to anticipate cavitation, and this requires the detection and identification of precursor features that occur just before cavitation starts.

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7 pages

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  • Artificial Neural Networks in Engineering, ANNIE '99, St. Louis, MO, November 7-10, 1999

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  • Other: DE00007463
  • Report No.: ORNL/CP-102987
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 7463
  • Archival Resource Key: ark:/67531/metadc709399

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • November 7, 1999

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • Feb. 15, 2016, 12:20 p.m.

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Kercel, S.W. Extracting a Whisper from the DIN: A Bayesian-Inductive Approach to Learning an Anticipatory Model of Cavitation, article, November 7, 1999; Oak Ridge, Tennessee. (digital.library.unt.edu/ark:/67531/metadc709399/: accessed October 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.