Neural network based refrigerant charge detection algorithm for vapor compression systems
First Claim
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) generating a data set 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;
(b) identifying 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;
(c) forming a revised data set that includes at least the steady-state data points;
(d) using 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;
(e) supplying 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
(f) deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge.
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Abstract
Methods and apparatus are provided for determining refrigerant charge in a vapor compressor system (VCS) of an aircraft. The methods and apparatus comprise the following steps of, and/or means for, generating a data set from historical data representative of a plurality of VCS operating conditions over time, identifying one or more steady-state data points in the generated data set, forming a revised data set that includes at least the steady-state data points, using principal components analysis (PCA) to derive values for a plurality of minimally correlated input variables, supplying 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 deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge.
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Citations
21 Claims
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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:
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(a) generating a data set 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; (b) identifying 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; (c) forming a revised data set that includes at least the steady-state data points; (d) using 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; (e) supplying 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 (f) deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for developing a model for determining refrigerant charge in a vapor compressor system (VCS) of an aircraft, the method comprising the steps of:
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(a) generating a data set 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; (b) identifying 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; (c) forming a revised data set that includes at least the steady-state data points; (d) dividing the steady-state data points according to particular ranges of VCS operating conditions, thereby creating a revised data subset for each range of VCS operating conditions; (e) calculating average values for the plurality of VCS operating variables for each data subset; (f) supplying the average values for the plurality of VCS operating variables to a nonlinear neural network model; and (g) deriving a simulator model characterizing a relationship between the plurality of VCS operating variables and the VCS refrigerant charge. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A system for determining the refrigerant charge in a vapor compressor system (VCS) of an aircraft, the system comprising:
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(a) means for generating a data set 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; (b) means for identifying 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; (c) means for forming a revised data set that includes at least the steady-state data points; (d) means for using 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; (e) means for supplying 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 (f) means for deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge. - View Dependent Claims (18)
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19. A program product comprising:
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(a) a program configured to determine a refrigerant charge in a vapor compressor system (VCS) of an aircraft via a simulator model derived from a data set from historical data representative of a plurality of VCS operating conditions over time, utilizing principal components analysis (PCA) for deriving minimally correlated input variables, and further utilizing a nonlinear neural network model; and (b) a computer-readable signal bearing media bearing the program.
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20. An apparatus comprising:
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(a) a processor; (b) a memory coupled to the processor; and (c) a program residing in memory and being executed by the processor, the program configured to provide a model for determining a refrigerant charge in a vapor compressor system (VCS) of an aircraft via a model through at least the following steps; (i) generating a data set 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 one or more values for a plurality of VCS operating variables reflecting operation of the VCS over a specific time period, and corresponding values for VCS refrigerant charge over the same time period; (ii) identifying 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; (iii) forming a revised data set that includes at least the steady-state data points; (iv) using 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; (v) supplying 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 (vi) deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge. - View Dependent Claims (21)
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Specification