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Neural network based refrigerant charge detection algorithm for vapor compression systems

  • US 7,680,751 B2
  • Filed: 05/31/2006
  • Issued: 03/16/2010
  • Est. Priority Date: 05/31/2006
  • Status: Expired due to Fees
First Claim
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1. A method of developing a model for determining refrigerant charge in a vapor compressor system (VCS) of an aircraft, the method comprising the steps of:

  • (a) receiving, in a processor, a data set generated from historical data representative of a plurality of VCS operating conditions over time, the generated data set comprising a plurality of data points, each data point comprising;

    (i) one or more values for a plurality of VCS operating variables reflecting operation of the VCS over a specific time period and corresponding to a specific set of operating conditions; and

    (ii) corresponding values for VCS refrigerant charge over the same time period; and

    (b) the processor processing the data to;

    (i) identify one or more steady-state data points in the generated data set, each steady-state data point corresponding to steady-state operation of the VCS;

    (ii) form a revised data set that includes at least the steady-state data points;

    (iii) use principal components analysis (PCA) to derive values for a plurality of minimally correlated input variables from the values for the plurality of VCS operating variables in the revised data set;

    (iv) supply the derived values for the plurality of minimally correlated input variables, and the corresponding values for the VCS refrigerant charge in the revised data set, to a nonlinear neural network model; and

    (v) derive a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge using the nonlinear neural network model.

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