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COMPUTER PROGRAM AND METHOD FOR DETECTING AND PREDICTING VALVE FAILURE IN A RECIPROCATING COMPRESSOR

  • US 20100106458A1
  • Filed: 10/28/2008
  • Published: 04/29/2010
  • Est. Priority Date: 10/28/2008
  • Status: Abandoned Application
First Claim
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1. A computer program stored on a computer-readable medium for predicting failure of a valve in a reciprocating compressor, the computer program comprising:

  • a code segment executable by the computer for monitoring a pressure signal produced by the valve of the reciprocating compressor;

    a code segment executable by the computer for applying a time-frequency analysis to the pressure signal so as to obtain a pressure waveform;

    a code segment executable by the computer for applying a wavelet transform to the pressure waveform so as to perform a feature selection analysis; and

    a code segment executable by the computer for training a plurality of neural networks so a s to select a best performing network operable to predict a behavior for the valve of the reciprocating compressor within a predetermined period of time, the code segment for training of the plurality of neural networks includinga code segment executable by the computer for initializing the plurality of neural networks by inputting a portion of the features selected from the feature selection analysis into each of the plurality of networks,a code segment executable by the computer for applying a gradient descent algorithm to each neural network to obtain a generalized error of the neural network,a code segment executable by the computer for selecting from each of the neural networks a plurality of high-performing networks,a code segment executable by the computer for applying a particle swarm optimization to enhance an accuracy of the selected high-performing networks,a code segment executable by the computer for creating an equal number of high-performing networks by mutating the high-performing networks selected from step (d3) using an evolutionary algorithm, anda code segment executable by the computer for repeating the code segments for training of the plurality of neural networks until the plurality of neural networks are trained to have a predetermined accuracy rate between an actual value and a desired value.

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