Process model error correction method and system

• US 20070203864A1
• Filed: 01/31/2006
• Published: 08/30/2007
• Est. Priority Date: 01/31/2006
• Status: Active Grant

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Grichnik, Anthony
• Granted Patent: US 7,505,949 B2
• Time in Patent Office: Days
• US Class Current: 706/44
• CPC Class Codes: F01N 9/005 using models instead of sensors to determine operating characteristics of exhaust systems, e.g. calculating catalyst temperature instead of measuring it directly
• CPC Class Codes: F02D 2041/1423 Identification of model or controller parameters
• CPC Class Codes: F02D 41/1405 Neural network control
• CPC Class Codes: F02D 41/1462 with determination means using an estimation
• CPC Class Codes: F02D 41/2432 Methods of calibration
• CPC Class Codes: G05B 17/02 electric

Abstract

A method is provided for a virtual sensor system. The method may include establishing a first process model indicative of interrelationships between a plurality of input parameters and a plurality of output parameters and establishing a second process model indicative of interrelationships between at least the plurality of input parameters and modeling errors of the first process model. The method may also include operating the first process model to generate values of the plurality of output parameters and simultaneously operating the second model to generate estimated deviations between the values of the plurality of output parameters and desired values of the plurality of output parameters. Further, the method may include compensating the values of the plurality of output parameters with the estimated deviations to generate the desired values of the plurality of output parameters.

Claims

1. A method for a virtual sensor system, comprising:

establishing a first process model indicative of interrelationships between a plurality of input parameters and a plurality of output parameters;

establishing a second process model indicative of interrelationships between at least the plurality of input parameters and modeling errors of the first process model;

operating the first process model to generate values of the plurality of output parameters;

simultaneously operating the second model to generate estimated deviations between the values of the plurality of output parameters and desired values of the plurality of output parameters; and

compensating the values of the plurality of output parameters with the estimated deviations to generate the desired values of the plurality of output parameters.

2. The method according to claim 1, wherein the second process model is indicative of interrelationships between the modeling errors and a combination of the plurality of input parameters and the values of the plurality of output parameters from the first process model.

3. The method according to claim 2, wherein the simultaneously operating includes:

obtaining the values of the plurality of output parameters from the first process model;

providing the values of the plurality of output parameters and the plurality of input parameters to the second process model; and

generating, by the second process model, the estimated deviations based on the interrelationships between the modeling errors and the combination of the plurality of input parameters and the values of the plurality of output parameters from the first process model.

4. The method according to claim 1, wherein establishing the first process model includes:

obtaining data records associated with one or more input variables and the plurality of output parameters;

selecting the plurality of input parameters from the one or more input variables;

generating a computational model indicative of the interrelationships between the plurality of input parameters and the plurality of output parameters;

determining desired statistical distributions of the plurality of input parameters of the computational model; and

recalibrating the plurality of input parameters based on the desired statistical distributions to define a desired input space.

5. The method according to claim 4, wherein selecting further includes:

pre-processing the data records; and

using a genetic algorithm to select the plurality of input parameters from the one or more input variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

6. The method according to claim 4, wherein generating further includes:

creating a neural network computational model;

training the neural network computational model using the data records; and

validating the neural network computation model using the data records.

7. The method according to claim 4, wherein determining further includes:

determining a candidate set of the input parameters with a maximum zeta statistic using a genetic algorithm; and

determining the desired distributions of the input parameters based on the candidate set,

wherein the zeta statistic ξ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.

8. The method according to claim 1, wherein the plurality of output parameters include at least a NO_x emission level.

9. The method according to claim 1, wherein the plurality of input parameters include at least intake manifold temperature, intake manifold pressure, ambient humidity, fuel rates, and engine speeds.

10. A computer system, comprising:

a database configured to store information relevant to the virtual sensor system; and

a processor configured to;

operate a first process model to generate values of a plurality of output parameters;

simultaneously operate a second model to generate estimated deviations between the values of the plurality of output parameters and desired values of the plurality of output parameters; and

compensate the values of the plurality of output parameters with the estimated deviations to generate the desired values of the plurality of output parameters.

11. The computer system according to claim 10, wherein the processor is configured to:

establish the first process model indicative of interrelationships between a plurality of input parameters and the plurality of output parameters; and

establish the second process model indicative of interrelationships between at least the plurality of input parameters and modeling errors of the first process model;

12. The computer system according to claim 11, wherein the second process model is indicative of interrelationships between the modeling errors and a combination of the plurality of input parameters and the values of the plurality of output parameters from the first process model.

13. The computer system according to claim 12, wherein, to simultaneously operate the second model, the processor is configured to:

obtain the values of the plurality of output parameters from the first process model;

provide the values of the plurality of output parameters and the plurality of input parameters to the second process model; and

generate the estimated deviations based on the interrelationships between the modeling errors and the combination of the plurality of input parameters and the values of the plurality of output parameters from the first process model.

14. The computer system according to claim 11, wherein, to establish the first process model, the processor is configured to:

obtain data records associated with one or more input variables and the plurality of output parameters;

select the plurality of input parameters from the one or more input variables;

generate a computational model indicative of the interrelationships between the plurality of input parameters and the plurality of output parameters;

determine desired statistical distributions of the plurality of input parameters of the computational model; and

recalibrate the plurality of input parameters based on the desired statistical distributions to define a desired input space.

15. The computer system according to claim 14, wherein, to select the plurality of input parameters, the processor is further configured to:

pre-process the data records; and

use a genetic algorithm to select the plurality of input parameters from the one or more input variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

16. The computer system according to claim 14, wherein, to generate the computational model, the processor is further configured to:

create a neural network computational model;

train the neural network computational model using the data records; and

validate the neural network computation model using the data records.

17. The computer system according to claim 14, wherein, to determine the desired statistical distributions, the processor is further configured to:

determine a candidate set of the input parameters with a maximum zeta statistic using a genetic algorithm; and

determine the desired distributions of the input parameters based on the candidate set,

wherein the zeta statistic ξ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i h represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| sensitivity of the jth output to the ith input of the computational model.

18. A work machine, comprising:

a power source configured to provide power to the work machine;

a control system configured to control the power source; and

a virtual sensor system including a first process model and a second process model, the virtual sensor system being configured to;

operate the first process model to generate values of a plurality of sensing parameters;

simultaneously operate the second model to generate estimated deviations between the values of the plurality of sensing parameters and desired values of the plurality of sensing parameters; and

compensate the values of the plurality of sensing parameters with the estimated deviations to generate the desired values of the plurality of sensing parameters,

wherein the control system controls the power source based upon the desired values of the plurality of sensing parameters.

19. The work machine according to claim 18, wherein the virtual sensor system is configured to:

establish the first process model indicative of interrelationships between a plurality of measured parameters and the plurality of sensing parameters; and

establish the second process model indicative of interrelationships between at least the plurality of measured parameters and modeling errors of the first process model;

20. The work machine according to claim 19, wherein the second process model is indicative of interrelationships between the modeling errors and a combination of the plurality of measured parameters and the values of the plurality of sensing parameters from the first process model.

21. The work machine according to claim 20, wherein, to simultaneously operate the second model, the virtual sensor system is configured to:

obtain the values of the plurality of sensing parameters from the first process model;

provide the values of the plurality of sensing parameters and the plurality of measured parameters to the second process model; and

generate the estimated deviations based on the interrelationships between the modeling errors and the combination of the plurality of measured parameters and the values of the plurality of sensing parameters from the first process model.

22. A computer-readable medium for use on a computer system, the computer-readable medium having computer-executable instructions for performing a method comprising:

establishing a first process model indicative of interrelationships between a plurality of input parameters and a plurality of output parameters;

establishing a second process model indicative of interrelationships between at least the plurality of input parameters and modeling errors of the first process model;

operating the first process model to generate values of the plurality of output parameters;

simultaneously operating the second model to generate estimated deviations between the values of the plurality of output parameters and desired values of the plurality of output parameters; and

compensating the values of the plurality of output parameters with the estimated deviations to generate the desired values of the plurality of output parameters.

23. The computer-readable medium according to claim 22, wherein the second process model is indicative of interrelationships between the modeling errors and a combination of the plurality of input parameters and the values of the plurality of output parameters from the first process model.

24. The method according to claim 23, wherein the simultaneously operating includes:

obtaining the values of the plurality of output parameters from the first process model;

providing the values of the plurality of output parameters and the plurality of input parameters to the second process model; and

generating, by the second process model, the estimated deviations based on the interrelationships between the modeling errors and the combination of the plurality of input parameters and the values of the plurality of output parameters from the first process model.

25. The method according to claim 22, wherein establishing the first process model includes:

obtaining data records associated with one or more input variables and the plurality of output parameters;

selecting the plurality of input parameters from the one or more input variables;

generating a computational model indicative of the interrelationships between the plurality of input parameters and the plurality of output parameters;

determining desired statistical distributions of the plurality of input parameters of the computational model; and

recalibrating the plurality of input parameters based on the desired statistical distributions to define a desired input space.