Virtual sensor based control system and method

• US 20090063087A1
• Filed: 08/31/2007
• Published: 03/05/2009
• Est. Priority Date: 08/31/2007
• Status: Active Grant

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Grichnik, Anthony J., Seskin, Michael
• Granted Patent: US 7,542,879 B2
• Time in Patent Office: Days
• US Class Current: 702/127
• CPC Class Codes: G05B 17/02 electric

Abstract

A method for a virtual sensor system corresponding to a target physical sensor is provided. The method may include selecting a plurality of measured parameters provided by a set of physical sensors based on operational characteristics of the virtual sensor system. The method may also include establishing a virtual sensor process model indicative of interrelationships between one or more sensing parameter and the plurality of measured parameters. Further the method may include obtaining a set of values corresponding to the plurality of measured parameters; calculating a value of the sensing parameter based upon the set of values corresponding to the plurality of measured parameters and the virtual sensor process model; and providing the value of the sensing parameter to a control system.

Claims

1. A method for a virtual sensor system corresponding to a target physical sensor, comprising:

selecting a plurality of measured parameters provided by a set of physical sensors based on operational characteristics of the virtual sensor system;

establishing a virtual sensor process model indicative of interrelationships between one or more sensing parameter and the plurality of measured parameters;

obtaining a set of values corresponding to the plurality of measured parameters;

calculating a value of the sensing parameter based upon the set of values corresponding to the plurality of measured parameters and the virtual sensor process model; and

providing the value of the sensing parameter to a control system.

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

obtaining data records corresponding to a plurality of physical sensors including the set of physical sensors and the target physical sensor;

separating the plurality of physical sensors into groups of physical sensors;

determining the operational characteristics of the virtual sensor system; and

selecting one or more group of physical sensors as the set of physical sensors.

3. The method according to claim 2, wherein selecting the one or more group of physical sensors further includes:

determining whether the operational characteristics indicate a closed loop control mechanism;

excluding the group of physical sensors including the target physical sensor if the operational characteristics indicate a closed loop control mechanism; and

selecting the group of physical sensors including the target physical sensor if the operational characteristics do not indicate a closed loop control mechanism.

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

obtaining data records associated with one or more input variables and the one or more sensing parameter;

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

generating a computational model indicative of the interrelationships between the plurality of measured parameters and the one or more sensing parameter;

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

recalibrating the plurality of measured 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 measured 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 measured parameters with a maximum zeta statistic using a genetic algorithm; and

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

wherein the zeta statistic ζ

is represented by;

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

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, further including:

separately obtaining values of the plurality of sensing parameters from the target physical sensor;

determining that the target physical sensor has failed; and

providing the values of the plurality of sensing parameters from the virtual sensor process model to the control system.

9. A computer system for establishing a virtual sensor system corresponding to a target physical sensor, comprising:

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

a processor configured to;

select a plurality of measured parameters provided by a set of physical sensors based on operational characteristics of the virtual sensor system;

establish the virtual sensor process model indicative of interrelationships between one or more sensing parameter and the plurality of measured parameters;

obtain a set of values corresponding to the plurality of measured parameters;

calculate a value of the sensing parameter based upon the set of values corresponding to the plurality of measured parameters and the virtual sensor process model; and

provide the value of the sensing parameter to a control system.

10. The computer system according to claim 9, wherein, to select the plurality of measured parameters, the processor is further configured to:

obtain data records corresponding to a plurality of physical sensors including the set of physical sensors and the target physical sensor;

separate the plurality of physical sensors into groups of physical sensors;

determine the operational characteristics of the virtual sensor system; and

select one or more group of physical sensors as the set of physical sensors.

11. The computer system according to claim 10, wherein, to select the one or more group of physical sensors, the processor is further configured to:

determine whether the operational characteristics indicate a closed loop control mechanism;

exclude the group of physical sensors including the target physical sensor if the operational characteristics indicate a closed loop control mechanism; and

select the group of physical sensors including the target physical sensor if the operational characteristics do not indicate a closed loop control mechanism.

12. The computer system according to claim 9, wherein, to establish the virtual sensor process model, the processor is further configured to:

obtain data records associated with one or more input variables and the one or more sensing parameter;

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

generate a computational model indicative of the interrelationships between the plurality of measured parameters and the one or more sensing parameter;

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

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

13. The computer system according to claim 12, wherein, to select the plurality of measured parameters, the processor is further configured to:

pre-process the data records; and

use a genetic algorithm to select the plurality of measured 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.

14. The computer system according to claim 12, wherein, to generate a 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.

15. The computer system according to claim 12, wherein, to determine the desired statistical distributions, the processor is further configured to:

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

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

wherein the zeta statistic ζ

is represented by;

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

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.

16. The computer system according to claim 9, wherein the processor is further configured to:

separately obtain values of the plurality of sensing parameters from the target physical sensor;

determine that the target physical sensor has failed; and

provide the values of the plurality of sensing parameters from the virtual sensor process model to the control system.

17. A machine, comprising:

a power source configured to provide power to the machine;

a control system configured to control the power source; and

a virtual sensor system corresponding to a target physical sensor, wherein the virtual sensor system includes a virtual sensor process model indicative of interrelationships between one or more sensing parameter and a plurality of measured parameters, and the virtual sensor system is configured to perform;

selecting a plurality of measured parameters provided by a set of physical sensors based on operational characteristics of the virtual sensor system;

establishing a virtual sensor process model indicative of interrelationships between one or more sensing parameter and the plurality of measured parameters;

obtaining a set of values corresponding to the plurality of measured parameters;

calculating a value of the sensing parameter based upon the set of values corresponding to the plurality of measured parameters and the virtual sensor process model; and

providing the value of the sensing parameter to a control system.

18. The machine according to claim 17, wherein selecting further includes:

obtaining data records corresponding to a plurality of physical sensors including the set of physical sensors and the target physical sensor;

separating the plurality of physical sensors into groups of physical sensors;

determining the operational characteristics of the virtual sensor system; and

selecting one or more group of physical sensors as the set of physical sensors.

19. The machine according to claim 18, wherein selecting the one or more group of physical sensors further includes:

determining whether the operational characteristics indicate a closed loop control mechanism;

excluding the group of physical sensors including the target physical sensor if the operational characteristics indicate a closed loop control mechanism; and

selecting the group of physical sensors including the target physical sensor if the operational characteristics do not indicate a closed loop control mechanism.