Control system and method

• US 20060229769A1
• Filed: 04/08/2005
• Published: 10/12/2006
• Est. Priority Date: 04/08/2005
• Status: Active Grant

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Grichnik, Anthony J., Seskin, Michael, Perez, Marco, Collins, Roy T.
• Granted Patent: US 7,565,333 B2
• Time in Patent Office: Days
• US Class Current: 701/1
• CPC Class Codes: G05B 17/02 electric

Abstract

A method is provided for a control system. The method may include obtaining data records associated one or more input variables and one or more output parameters, and selecting one or more input parameters from the one or more input variables. The method may also include generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records, and determining desired respective statistical distributions of the one or more input parameters of the computational model.

Claims

1. A method for a control system, comprising:

obtaining data records associated one or more input variables and one or more output parameters;

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

generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records; and

determining desired respective statistical distributions of the input parameters of the computational model.

2. The method according to claim 1, further including:

recalibrating the one or more input parameters based on the desired statistical distributions.

3. The method according to claim 2, further including:

controlling one or more hardware devices using the one or more output parameters.

4. The method according to claim 2, further including:

generating a second computational model indicative of referential interrelationships between the one or more input parameters and the one or more output parameters based on the data records.

5. The method according to claim 4, further including:

controlling one or more hardware devices using the values of the one or more output parameters from both the computational model and the second computational model.

6. The method according to claim 5, wherein controlling includes:

determining a difference between the values of the one or more output parameters from the computational model and the values of the one or more output parameters from the second computational model;

determining whether the difference is beyond a predetermined threshold; and

deciding a failure of the computational model if the difference is beyond the threshold.

7. The method according to claim 6, further including:

controlling the hardware devices using the values of the output parameters from the second computational model.

8. The method according to claim 1, wherein selecting further includes:

pre-processing the data records; and

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

9. The method according to claim 1, 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.

10. The method according to claim 1, wherein determining further includes:

determining a candidate set of 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 ζ

is represented by;

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

provided that {overscore (x)}_i represents a mean of an ith input;

{overscore (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.

11. A control system, comprising:

one or more input elements configured to accept respective one or more input parameters to the control system;

one or more output elements configured to accept respective one or more output parameters; and

a processor configured to control one or more hardware devices using the one or more output parameters based on a control model, wherein the control model is created by;

obtaining data records associating the one or more input parameters and one or more output parameters;

generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records;

determining desired respective statistical distributions of the one or more input parameters of the computational model; and

recalibrating the one or more input parameters based on the desired statistical distributions.

12. The control system according to claim 11, wherein creating the control model further includes:

generating a second computational model indicative of referential interrelationships between the one or more input parameters and the one or more output parameters based on the data records.

13. The control system according to claim 12, the processor is further configured to:

determine a difference between the values of the one or more output parameters from the computational model and the values of the one or more output parameters from the second computational model;

determine whether the difference is beyond a predetermined threshold;

decide that the computational model has failed if the difference is beyond the threshold; and

control the hardware devices using the values of the one or more output parameters from the second computational model.

14. The control system according to claim 13, wherein the processor is further configured to:

control the hardware devices using the values of the one or more output parameters from the computational model if the difference is not beyond the threshold.

15. The control system according to claim 11, 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.

16. The control system according to claim 11, wherein determining further includes:

determining a candidate set of 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 ζ

is represented by;

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

provided that {overscore (x)}_i represents a mean of an ith input;

{overscore (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.

17. A work machine, comprising:

an engine; and

an engine control system, wherein the engine control system includes a processor configured to;

obtain respective values of one or more input parameters;

derive respective values of one or more output parameters based on the values of one or more input parameters and a first computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters; and

control the engine using the values of the output parameters.

18. The work machine according to claim 17, wherein the processor is further configured to:

derive respective values of the one or more output parameters based on the values of one or more input parameters and a second computational model indicative of referential interrelationships between the one or more input parameters and the one or more output parameters;

determine a difference between the values of the one or more output parameters from the first computational model and the values of the one or more output parameters from the second computational model;

determine whether the difference is beyond a predetermined threshold;

decide that the first computational model has failed if the difference is beyond the threshold; and

control the engine using the values of the one or more output parameters from the second computational model.

19. The work machine according to claim 18, the processor is further configured to:

control the engine using the values of the one or more output parameters from the first computational model if the difference is not beyond the threshold.

20. A computer system, comprising:

a database containing data records associating one or more input variables and one or more output parameters; and

a processor configured to;

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

generate a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records;

determine desired respective statistical distributions of the one or more input parameters of the computational model; and

recalibrate the one or more input parameters based on the desired statistical distributions.

21. The computer system according to claim 20, wherein, to select one or more the input parameters, the processor is further configured to:

pre-process the data records; and

use a genetic algorithm to select one or more 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.

22. The computer system according to claim 20, 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.

23. The computer system according to claim 20, wherein, to determine desired respective statistical distributions, the processor is further configured to:

determine a candidate set of 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 ζ

is represented by;

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

provided that {overscore (x)}_i represents a mean of an ith input;

{overscore (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.