Automated model configuration and deployment system for equipment health monitoring

• US 7,640,145 B2
• Filed: 04/24/2006
• Issued: 12/29/2009
• Est. Priority Date: 04/25/2005
• Status: Active Grant

• Current Assignee: SmartSignal Corporation (Emerson Electric Company)
• Original Assignee: SmartSignal Corporation (Emerson Electric Company)
• Inventors: Xu, Xiao, Herzog, James P., Wegerich, Stephan W., Pipke, Robert Matthew, Wolosewicz, Andre
• Primary Examiner(s): Rodriguez; Paul L
• Assistant Examiner(s): Craig; Dwin M
• Application Number:
  US11/409,920
• Publication Number:
  US 20070005311A1
• Time in Patent Office: 1,345 Days
• Field of Search: 703/2
• US Class Current: 703/2
• CPC Class Codes: G05B 17/02 electric
• 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

Abstract

A method for systematically configuring and deploying an empirical model used for fault detection and equipment health monitoring. The method is driven by a set of data preprocessing and model performance metrics subsystems that when applied to a raw data set, produce an optimal empirical model.

Claims

1. A method for implementing a monitoring system for monitoring equipment health, comprising the steps of:

generating a first empirical model from reference data representing a piece of equipment;

generating a second empirical model from the reference data;

generating at least one performance metric for said first empirical model including intentionally adding a disturbance to at least one variable;

generating at least one performance metric for said second empirical model including intentionally adding a disturbance to at least one variable;

upon comparing the at least one performance metrics from said first and said second empirical models, selecting one of them to use for monitoring the piece of equipment.

2. A method according to claim 1, wherein the at least one performance metric is selected from the set comprising a measure of robustness, a measure of spillover, and a measure of minimum detectable shift.

3. A method according to claim 2, wherein the at least one performance metric is a measure of robustness.

4. A method according to claim 3, wherein said measure of robustness comprises:

providing a set of multivariate, normal test data samples representative of normal operation of said piece of equipment;

adding the disturbance to at least one variable of at least some of said normal test data samples for forming disturbed normal test data;

generating estimates with each said empirical model of normal test data;

generating estimates with each said empirical model of said disturbed normal test data; and

for each said empirical model, differencing the estimates of said disturbed normal test data with the estimates of said normal test data to determine a measure of robustness for each disturbed variable.

5. A method according to claim 4, further comprising the steps of determining an overall model robustness for each said empirical model by combining said measures of robustness for every disturbed variable in each said empirical model.

6. A method according to claim 4, wherein the measure of robustness for a disturbed variable is determined by summing the absolute values of all differences between estimates for disturbed normal test data and estimates for normal test data for the disturbed variable, and dividing the sum by the quantity of the count of all disturbed samples multiplied by the disturbance size.

7. A method according to claim 6, wherein for each sample of said disturbed normal test data, a disturbance is added to at most one variable.

8. A method according to claim 6, wherein said first empirical model is a kernel based model.

9. A method according to claim 8, wherein said first empirical model is a similarity-based model.

10. A method according to claim 2, wherein the at least one performance metric is a measure of spillover.

11. A method according to claim 10, wherein said measure of spillover comprises:

providing a set of multivariate, normal test data samples representative of normal operation of said piece of equipment;

adding the disturbance to at least one variable of at least some of said normal test data samples for forming disturbed normal test data;

generating estimates with each said empirical model of normal test data;

generating estimates with each said empirical model of said disturbed normal test data; and

for each said empirical model, differencing the estimates of at least one other variable for said disturbed normal test data with the estimates of the other variable for said normal test data, determining normalized Root Mean Square (RMS) for said differences, and dividing by a measure of variance in the disturbed variable absent the disturbance, to determine a measure of impact on the other variable for the disturbed variable.

12. A method according to claim 11, further comprising averaging measures of impact across all other variables for the disturbed variable to provide a measure of spillover for said disturbed variable.

13. A method according to claim 2, wherein the at least one performance metric is a measure of minimum detectable shift.

14. A method according to claim 13, wherein said measure of minimum detectable shift comprises:

providing a set of multivariate, normal test data samples representative of normal operation of said piece of equipment;

adding the disturbance to at least one target variable of at least some of said normal test data samples for forming disturbed normal test data;

generating estimates with each said empirical model of normal test data;

generating estimates with each said empirical model of said disturbed normal test data;

for each said empirical model, differencing the estimates of said disturbed normal test data with the estimates of said normal test data to determine a measure of robustness for the target variable;

determining a bias in estimates for the target variable;

determining a measure of variance for the target variable; and

determining a minimum detectable shift for the target variable equivalent to the measure of robustness for the target variable plus the quantity of the estimate bias for the target variable multiplied by the measure of variance in the target variable.

15. A method according to claim 14, wherein said first empirical model is a kernel-based model.

16. A method according to claim 15, wherein said first empirical model is a similarity-based model.

17. An apparatus for determining the performance of a model based monitoring system for monitoring equipment health, comprising:

a processor for executing computer code;

a memory for storing reference data and test data representative of a piece of equipment;

a first computer code module disposed to cause said processor to generate a model of said piece of equipment from said reference data;

a second computer code module disposed to cause said processor to use said model to generate normal estimates of said test data; and

a third computer code module disposed to cause said processor to intentionally add a disturbance value to one variable per sample, for at least some of the samples comprising said test data, generate an estimate of each such sample, compare the estimate to the corresponding one of said normal estimates for said test data, and generate a model performance metric therefrom.

18. An apparatus according to claim 17, wherein said model performance metric is a measure of robustness.

19. An apparatus according to claim 18, wherein said third computer code module is further disposed to cause said processor to sum the absolute values of all differences between estimates for a variable with the disturbance value and estimates for the same variable without the disturbance value, and divide the sum by the quantity of the count of all samples wherein that variable was disturbed multiplied by the disturbance value.

20. An apparatus according to claim 17, wherein said model performance metric is a measure of spillover.

21. An apparatus according to claim 20, wherein said third computer code module is further disposed to cause said processor to determine a spillover from a first variable to a second variable by subtracting the estimates of said second variable for samples in which said first variable has the disturbance value added to it, from the estimates of said second variable in the same samples when no disturbance value has been added to any variable of such samples, determining a normalized Root Mean Square (RMS) for the resulting differences, and dividing by a measure of variance of said first variable.

22. An apparatus according to claim 17, wherein said model performance metric is a measure of minimum detectable shift.

23. An apparatus according to claim 22, wherein said third computer code module is further disposed to cause said processor to determine a bias in estimates for a target variable, determine a measure of variance for the target variable, and determine a minimum detectable shift for the target variable equivalent to a measure of robustness for the target variable plus the quantity of the estimate bias for the target variable multiplied by the measure of variance in the target variable.

24. An apparatus according to claim 17, wherein said model is a kernel-based model.

25. An apparatus according to claim 24, wherein said model is a similarity-based model.

26. A method for implementing a monitoring system for monitoring equipment health, comprising the steps of:

generating a first empirical model from reference data representing a piece of equipment;

generating a second empirical model from the reference data;

generating at least one measure of spillover for said first empirical model, comprising;

providing a set of multivariate, normal test data samples representative of normal operation of said piece of equipment,

adding a disturbance to at least one variable of said normal test data samples for forming disturbed normal test data,

generating estimates with each said empirical model of normal test data,

generating estimates with each said empirical model of disturbed normal test data,

for each said empirical model, differencing the estimates of at least one other variable for said disturbed normal test data with the estimates of the other variable for said normal test data,

determining normalized Root Mean Square (RMS) for said differences, and

dividing by a measure of variance in the disturbed variable absent the disturbance, to determine a measure of impact on the other variable for the disturbed variable;

generating at least one measure of spillover for said second empirical model; and

upon comparing the at least one measure of spillover from said first and said second empirical models, selecting one of them to use for monitoring the piece of equipment.

27. A method according to claim 26, further comprising averaging measures of impact across all other variables for the disturbed variable to provide a measure of spillover for said disturbed variable.

28. A method for implementing a monitoring system for monitoring equipment health, comprising the steps of:

generating a first empirical model from reference data representing a piece of equipment;

generating a second empirical model from the reference data;

generating at least one measure of minimum detectable shift for said first empirical model, comprising;

providing a set of multivariate, normal test data samples representative of normal operation of said piece of equipment,

adding a disturbance to at least one target variable of said set of normal test data, over at least some of said normal test data samples, to form disturbed normal test data,

generating estimates with each said empirical model of said normal test data,

generating estimates with each said empirical model of said disturbed normal test data,

for each said empirical model, differencing the estimates of said disturbed normal test data with the estimates of said normal test data to determine a measure of robustness for the target variable,

determining a bias in estimates for the target variable,

determining a measure of variance for the target variable, and

determining a minimum detectable shift for the target variable equivalent to the measure of robustness for the target variable plus the quantity of the estimate bias for the target variable multiplied by the measure of variance in the target variable;

generating at least one measure of minimum detectable shift for said second empirical model; and

upon comparing the at least one performance metrics from said first and said second empirical models, selecting one of them to use for monitoring the piece of equipment.

29. A method according to claim 28, wherein said first empirical model is a kernel-based model.

30. A method according to claim 29, wherein said first empirical model is a similarity-based model.

31. An apparatus for determining the performance of a model based monitoring system for monitoring equipment health, comprising:

a processor for executing computer code;

a memory for storing reference data and test data representative of a piece of equipment;

a first computer code module disposed to cause said processor to generate a model of said piece of equipment from said reference data;

a second computer code module disposed to cause said processor to use said model to generate normal estimates of said test data; and

a third computer code module to generate a robustness metric by being disposed to cause said processor to;

add a disturbance value to one variable per sample, for at least some of the samples comprising said test data,

generate an estimate of each such sample,

compare the estimate to the corresponding one of said normal estimates for said test data,

sum the absolute values of all differences between estimates for a variable with the disturbance value and estimates for the same variable without the disturbance value, and

divide the sum by the quantity of the count of all samples wherein that variable was disturbed multiplied by the disturbance value.

32. An apparatus for determining the performance of a model based monitoring system for monitoring equipment health, comprising:

a processor for executing computer code;

a memory for storing reference data and test data representative of a piece of equipment;

a first computer code module disposed to cause said processor to generate a model of said piece of equipment from said reference data;

a second computer code module disposed to cause said processor to use said model to generate normal estimates of said test data; and

a third computer code module to generate a spillover metric by being disposed to cause said processor to;

add a disturbance value to one variable per sample, for at least some of the samples comprising said test data,

generate an estimate of each such sample,

compare the estimate to the corresponding one of said normal estimates for said test data,

determine a measure of spillover from a first variable to a second variable by subtracting the estimates of said second variable for samples in which said first variable has the disturbance value added to it, from the estimates of said second variable in the same samples when no disturbance value has been added to any variable of such samples,

determining a normalized Root Mean Square (RMS) for the resulting differences, and

dividing by a measure of variance of said first variable.

33. An apparatus for determining the performance of a model based monitoring system for monitoring equipment health, comprising:

a processor for executing computer code;

a memory for storing reference data and test data representative of a piece of equipment;

a first computer code module disposed to cause said processor to generate a model of said piece of equipment from said reference data;

a second computer code module disposed to cause said processor to use said model to generate normal estimates of said test data; and

a third computer code module disposed to cause said processor to;

add a disturbance value to one variable per sample, for at least some of the samples comprising said test data,

generate an estimate of each such sample,

compare the estimate to the corresponding one of said normal estimates for said test data,

determine a bias in estimates for a target variable,

determine a measure of variance for the target variable, and

determine a minimum detectable shift for the target variable equivalent to a measure of robustness for the target variable plus the quantity of the estimate bias for the target variable multiplied by the measure of variance in the target variable.