Diagnostic and prognostic method and system

• US 20060230313A1
• 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, Willden, Wade
• Granted Patent: US 7,499,777 B2
• Time in Patent Office: Days
• US Class Current: 714/26
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

A method for diagnosing a system is provided. The method may include obtaining a plurality of input parameters from the system. The system may generate actual values of one or more output parameters based on the plurality of input parameters. The method may also include independently deriving values of the one or more output parameters, based on the plurality of input parameters, using a computational model, and detecting a divergence between the derived values and the actual values. Further, the method may include identifying respective desired statistic distributions of the plurality of input parameters, based on the actual values of the one or more output parameters, using the computational model.

Claims

1. A method for diagnosing a system, comprising:

obtaining a plurality of input parameters from the system, wherein the system generates actual values of one or more output parameters based on the plurality of input parameters;

independently deriving values of the one or more output parameters, based on the plurality of input parameters, using a computational model;

detecting a divergence between the derived values and the actual values; and

identifying respective desired statistical distributions of the plurality of input parameters, based on the actual values of the one or more output parameters, using the computational model.

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

identifying a potential failure associated with one or more of the input parameters by comparing the identified desired statistical distributions with original desired statistical distributions of the plurality of input parameters of the computational model.

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

performing a predetermined diagnostic action corresponding to the potential failure.

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

determining whether any of the plurality of input parameters is out of a predetermined input space prior to identifying the respective desired statistical distributions, wherein the input space is determined based on original desired statistical distributions of the plurality of input parameters of the computational model; and

determining the derived values of the one or more output parameters inconclusive for identifying potential failures if any of the plurality of input parameters is out of the input space.

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

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

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

wherein the zeta statistic ξ

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 parameter from the plurality of input parameters;

{overscore (x)}_j represents a mean of a jth output parameter from the one or more output parameters;

σ

_i represents a standard deviation of the ith input parameter from the plurality of input parameters;

σ

_j represents a standard deviation of thejth output parameter from the one or more output parameters; and

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

6. The method according to claim 1, wherein the computational model is generated by:

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

selecting a subset of input parameters from the plurality of input parameters;

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

determining desired respective statistical distributions of the subset of input parameters of the computational model based on a zeta statistic of the computational model.

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

pre-processing the data records; and

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

8. The method according to claim 6, 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.

9. The method according to claim 6, wherein determining further includes:

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

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

wherein the zeta statistic ξ

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.

10. A method for providing a prognosis associated with a system, comprising:

obtaining a plurality of input parameters from the system, wherein the system generates actual values of one or more output parameters based on the plurality of input parameters;

independently deriving values of the one or more output parameters using a computational model based on the plurality of input parameters;

estimating future values of the one or more output parameters of the system by calculating a trend of differences between the actual values and the derived values;

detecting a potential divergence between the future values and the derived values; and

identifying respective desired statistic distributions of the plurality of input parameters using the computational model based on the future values of the one or more output parameters.

11. The method according to claim 10, further including:

predicting a potential failure associated with one or more input parameters by comparing the identified desired statistical distributions with original desired statistical distributions of the plurality of input parameters of the computational model.

12. The method according to claim 11, further including:

notifying a user of the system of the potential failure.

13. The method according to claim 10, wherein the identifying further includes:

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

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

wherein the zeta statistic ξ

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 parameter from the plurality of input parameters;

{overscore (x)}_j represents a mean of a jth output parameter from the one or more output parameters;

σ

_i represents a standard deviation of the ith input parameter from the plurality of input parameters;

σ

_j represents a standard deviation of the jth output parameter from the one or more output parameters; and

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

14. The method according to claim 10, wherein the computational model is generated by:

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

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

determining original desired respective statistical distributions of the subset of input parameters of the computational model based on a zeta statistic of the computational model.

15. The method according to claim 14, 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 method according to claim 14, wherein determining further includes:

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

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

wherein the zeta statistic ξ

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 diagnostic and prognostic system, comprising:

a plurality of input elements configured to accept a respective plurality of input parameters from a system being diagnosed;

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

a processor configured to diagnose and predict a failure associated with any of the plurality of input parameters based on a diagnostic model, wherein the diagnostic model is created by;

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

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

determining original desired respective statistical distributions of the plurality of input parameters of the computational model based on a zeta statistic of the computational model.

18. The diagnostic and prognostic system according to claim 17, wherein the processor is further configured to:

obtain actual values of the one or more output parameters from the system being diagnosed;

independently derive values of the one or more output parameters using the computational model based on the plurality of input parameters;

detect a divergence between the derived values and the actual values;

identify respective desired statistic distributions of the plurality of input parameters using the computational model based on the actual values of the one or more output parameters; and

identify a potential failure associated with any of the plurality of input parameters by comparing the identified desired statistical distributions with the original desired statistical distributions of the plurality of input parameters.

19. The diagnostic and prognostic system according to claim 17, wherein the processor is further configured to:

obtain actual values of the one or more output parameters from the system being diagnosed;

independently derive values of the one or more output parameters using the computational model based on the plurality of input parameters;

estimate future values of the one or more output parameters of the system by calculating a trend of differences between the actual values and the derived values;

detect a potential divergence between the future values and the derived values;

identify respective desired statistical distributions of the plurality of input parameters using the computational model based on the future values of the one or more output parameters; and

predict a potential failure associated with any of the plurality of input parameters by comparing the identified desired statistical distributions with the original desired statistical distributions of the plurality of input parameters.

20. The control system 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 statistical distributions of the input parameters based on the candidate set,

wherein the zeta statistic ξ

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.

21. A work machine, comprising:

an engine;

an electronic control module configured to control operation of the engine by using actual values of one or more output parameters based on a plurality of input parameters; and

a diagnostic and prognostic system, wherein the diagnostic and prognostic system includes a processor configured to;

obtain the plurality of input parameters;

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

identify a potential failure associated with any of the plurality of input parameters based on a zeta statistic of the computational model.

22. The work machine according to claim 21, wherein, to identify the potential failure, the processor is further configured to:

obtain actual values of the one or more output parameters from the electronic control module;

independently derive values of the one or more output parameters using the computational model based on the plurality of input parameters;

detect a divergence between the derived values and the actual values;

identify respective desired statistic distributions of the plurality of input parameters using the computational model based on the actual values of the one or more output parameters and the zeta statistic of the computational model; and

identify a potential failure associated with any of the plurality of input parameters by comparing the identified desired statistical distributions with the original desired statistical distributions of the plurality of input parameters.

23. The Work machine according to claim 21, wherein the processor is further configured to predict a potential failure associated any of the plurality of the input parameters by:

obtaining actual values of the one or more output parameters from the electronic control module;

independently deriving values of the one or more output parameters using the computational model based on the plurality of input parameters;

estimating future values of the one or more output parameters of the system by calculating a trend of differences between the actual values and the derived values;

detecting a potential divergence between the future values and the derived values;

identifying respective desired statistic distributions of the plurality of input parameters using the computational model based on the future values of the one or more output parameters; and

predicting a potential failure associated with any of the plurality of input parameters by comparing the identified desired statistical distributions with the original desired statistical distributions of the plurality of input parameters.