Batch statistics process model method and system

• US 20070061144A1
• Filed: 08/30/2005
• Published: 03/15/2007
• Est. Priority Date: 08/30/2005
• Status: Abandoned Application

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Seskin, Michael, Grichnik, Anthony, Jayaram, Suresh
• Application Number:
  US11/213,798
• Publication Number:
  US 20070061144A1
• Time in Patent Office: Days
• US Class Current: 704/256.800
• CPC Class Codes: G05B 17/02 electric

Abstract

A method is provided for process modeling. The method may include obtaining batch statistics data records associated with 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 input parameters of the computational model.

Claims

1. A method for process modeling, comprising:

obtaining batch statistics data records associated with 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, wherein obtaining batch statistics data records includes obtaining mean and standard deviation of the input variables and the output parameters.

3. The method according to claim 1, wherein the input parameters are represented by mean and standard deviation values of a plurality of sample groups with respective sample sizes.

4. The method according to claim 1, wherein the output parameters are represented by mean and standard deviation values of a plurality of sample groups with respective sample sizes.

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

pre-processing the batch statistics data records; and

selecting 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.

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

calculating mahalanobis distances of the normal data set and the abnormal data set based on mean and standard deviation of the subset of variables;

setting up a genetic algorithm; and

identifying a desired subset of the input variables by performing the genetic algorithm based on the mahalanobis distances such that the genetic algorithm converges.

7. The method according to claim 1, wherein generating further includes:

creating a neural network computational model;

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

validating the neural network computation model using the batch statistics data records.

8. 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;

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

provided that I_i represents a mean of an ith input;

O_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.

9. A computer system, comprising:

a database containing batch statistics data records associating with 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 batch statistics data records; and

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

10. The method according to claim 9, wherein the batch statistics data records include mean and standard deviation of the input parameters and the output parameters.

11. The method according to claim 9, wherein the input parameters are represented by mean and standard deviation values of a plurality of sample groups with respective sample sizes.

12. The method according to claim 9, wherein the output parameters are represented by mean and standard deviation values of a plurality of sample groups with respective sample sizes.

13. The computer system according to claim 9, wherein, to select one or more the input parameters, the processor is further configured to:

pre-process the batch statistics data records; and

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 batch statistics data records.

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

calculate mahalanobis distances of the normal data set and the abnormal data set based on mean and standard deviation of the subset of variables;

set up a genetic algorithm; and

identify a desired subset of the input variables by performing the genetic algorithm based on the mahalanobis distances such that the genetic algorithm converges.

15. The computer system according to claim 9, 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 batch statistics data records; and

validate the neural network computation model using the batch statistics data records.

16. The method according to claim 9, 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;

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

provided that I_i represents a mean of an ith input;

O_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 computer-readable medium for use on a computer system configured to perform process modeling procedure, the computer-readable medium having computer-executable instructions for performing a method comprising:

obtaining batch statistics data records associated with 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 batch statistics data records; and

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

18. The computer-readable medium according to claim 17, wherein the input and output parameters are represented by mean and standard deviation values of a plurality of sample groups with respective sample sizes.

19. The computer-readable medium according to claim 17, wherein selecting further includes:

pre-processing the batch statistics data records to generate a normal data set and an abnormal data set of the batch statistics data records;

calculating mahalanobis distances of the normal data set and the abnormal data set based on mean and standard deviation of the subset of variables;

setting up a genetic algorithm; and

identifying a desired subset of the input variables by performing the genetic algorithm based on the mahalanobis distances such that the genetic algorithm converges.

20. The computer-readable medium according to claim 17, 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;

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

provided that I_i represents a mean of an ith input;

O_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.