Probabilistic modeling system for product design

• US 20090024367A1
• Filed: 07/17/2007
• Published: 01/22/2009
• Est. Priority Date: 07/17/2007
• Status: Active Grant

• Current Assignee: Caterpillar Incorporated
• Sponsoring Entity: Caterpillar Incorporated
• Inventors: Jayachandran, Amit, Grichnik, Anthony J., Seskin, Michael
• GRANTED PATENT: US 7,831,416 B2
• Time in USPTO: 0 Days
• US Class Current: 703/2
• CPC Class Codes: G06F 17/5009 : using simulation
• CPC Class Codes: G06F 2217/10 : Probabilistic or stochastic CAD

Abstract

A method is provided for designing a product. The method may include obtaining data records relating to one or more input variables and one or more output parameters associated with the product; and pre-processing the data records based on characteristics of the input variables. The method may also include selecting one or more input parameters from the one or more input variables; and 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. Further, the method may include providing a set of constraints to the computational model representative of a compliance state for the product; and using the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters and the one or more output parameters, wherein the one or more input parameters and the one or more output parameters represent a design for the product.

Claims

1. . A method for designing a product, comprising:

obtaining data records relating to one or more input variables and one or more output parameters associated with the product;

pre-processing the data records based on characteristics of the input variables;

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;

providing a set of constraints to the computational model representative of a compliance state for the product; and

using the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters and the one or more output parameters, wherein the one or more input parameters and the one or more output parameters represent a design for the product.

2. . The method of claim 1, wherein pre-processing further include:

determining a plurality of time lags of an input variable;

calculating a plurality correlation values between the input variable and the output parameters corresponding to the plurality of time lags; and

selecting a desired time lag from the plurality of time lags based on the correlation values.

3. . The method of claim 2, further including:

adjusting the data records based on the desired time lag.

4. . The method of claim 1, wherein pre-processing further include:

separating the data records into a plurality of data groups;

calculating a corresponding plurality of cluster centers of the data groups;

determining a respective plurality of distances between a data record and the plurality of cluster centers; and

processing the data records based on the plurality of distances.

5. . The method of claim 4, wherein replacing includes:

creating a distance matrix with a plurality of columns respectively representing the plurality of data groups and a plurality of rows respectively representing the data records, each element of the distance matrix being a distance between a data record and a cluster center of a data group; and

replacing the data records with the distance matrix.

6. . The method of claim 1, further including:

using the computation model to generate nominal values for the one or more input parameters and the one or more output parameters.

7. . The method of claim 6, further including modifying the design for the product by adjusting at least one of the statistical distributions and the nominal values for any of the one or more input parameters and the one or more output parameters.

8. . The method of claim 1, wherein using the computational model further includes:

obtaining respective ranges of the input parameters;

creating a plurality of model data records based on the respective ranges of the input parameters;

determining a candidate set of values of input parameters using the plurality of model data records with a maximum zeta statistic using a genetic algorithm; and

determining the statistical distributions of the one or more input parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

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

provided that 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.

9. . The method of claim 8, further including:

comparing the statistical distributions of the input parameters with the respective ranges of input parameters; and

determining whether the statistical distributions of the input parameters match the respective ranges of input parameters.

10. . The method of claim 9, further including:

if the statistical distributions of the input parameters do not match the respective ranges of the input parameters, changing the respective ranges of the input parameters to the same as the statistical distributions of the input parameters; and

re-determining the statistical distributions of the input parameters based on the changed respective ranges until the statistical distributions of the input parameters match the respective range of the input parameters.

11. . A computer readable medium including a set of instructions for enabling a processor to:

obtain data records relating to one or more input variables and one or more output parameters associated with the product;

pre-process the data records based on characteristics of the input variables;

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;

provide a set of constraints to the computational model representative of a compliance state for the product; and

use the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters and the one or more output parameters, wherein the one or more input parameters and the one or more output parameters represent a design for the product.

12. . The computer readable medium of claim 11, wherein the instructions for enabling the processor to pre-process the data records further enable the processor to:

determine a plurality of time lags of an input variable;

calculate a plurality correlation values between the input variable and the output parameters corresponding to the plurality of time lags; and

select a desired time lag from the plurality of time lags based on the correlation values.

13. . The computer readable medium of claim 12, wherein the instructions further enable the processor to:

adjust the data records based on the desired time lag.

14. . The computer readable medium of claim 11, wherein the instructions for enabling the processor to pre-process the data records further enable the processor to:

separate the data records into a plurality of data groups;

calculate a corresponding plurality of cluster centers of the data groups;

determine a respective plurality of distances between a data record and the plurality of cluster centers; and

process the data records based on the plurality of distances.

15. . The computer readable medium of claim 14, wherein the instructions further enable the processor to:

create a distance matrix with a plurality of columns respectively representing the plurality of data groups and a plurality of rows respectively representing the data records, each element of the distance matrix being a distance between a data record and a cluster center of a data group; and

replace the data records with the distance matrix.

16. . A computer-based product design system, comprising:

a database containing data records relating one or more input variables and one or more output parameters associated with a product to be designed; and

a processor configured to;

obtain the data records relating to the one or more input variables and the one or more output parameters associated with the product;

pre-process the data records based on characteristics of the input variables;

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;

provide a set of constraints to the computational model representative of a compliance state for the product; and

use the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters and the one or more output parameters, wherein the one or more input parameters and the one or more output parameters represent a design for the product.

17. . The computer-based product design system of claim 16, wherein to pre-process the data records, the processor is further configured to:

determine a plurality of time lags of an input variable;

calculate a plurality correlation values between the input variable and the output parameters corresponding to the plurality of time lags; and

select a desired time lag from the plurality of time lags based on the correlation values.

18. . The computer-based product design system of claim 17, wherein the processor is further configured to:

adjust the data records based on the desired time lag.

19. . The computer-based product design system of claim 16, wherein to pre-process the data records, the processor is further configured to:

separate the data records into a plurality of data groups;

calculate a corresponding plurality of cluster centers of the data groups;

determine a respective plurality of distances between a data record and the plurality of cluster centers; and

process the data records based on the plurality of distances.

20. . The computer-based product design system of claim 19, wherein the processor is further configured to:

create a distance matrix with a plurality of columns respectively representing the plurality of data groups and a plurality of rows respectively representing the data records, each element of the distance matrix being a distance between a data record and a cluster center of a data group; and

replace the data records with the distance matrix.