Asset degradation model baselinening system and method

• US 10,358,983 B2
• Filed: 04/19/2016
• Issued: 07/23/2019
• Est. Priority Date: 04/19/2016
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (General Electric Company)
• Original Assignee: General Electric Company
• Inventors: Block, Frederick William, Szepek, Scott William, Seely, William Forrester, Dokucu, Mustafa Tekin, Raffensperger, John Joseph
• Primary Examiner(s): Nguyen, Van H
• Application Number:
  US15/132,963
• Publication Number:
  US 20170300018A1
• Time in Patent Office: 1,190 Days
• Field of Search: None
• CPC Class Codes: F02C 9/00 Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants controlling air intakes F02C7/057; controlling turbines F01D; controlling compressors F04D27/00; controlling in general G05
• CPC Class Codes: F05D 2260/81 Modelling or simulation
• CPC Class Codes: G05B 19/4065 Monitoring tool breakage, life or condition
• CPC Class Codes: G05B 2219/37255 Using fuzzy logic techniques

Abstract

A system includes a model-based control system configured to receive data relating to parameters of a machinery via a plurality of sensors coupled to the machinery and select one or more models configured to generate a desired parameter of the machinery based on a determined relationship between the parameters and the desired parameter. The one or more models represent a performance of a device of the machinery. The model-based control system is configured to generate the desired parameter using the data and the one or more models control a plurality of actuators coupled to the machinery based on the desired parameter. Further, the model-based control system is configured to empirically tune the one or more models based on the data, the one or more parameters, and the desired parameter, compare the empirical tuning to a baseline tuning, and determine an operational state of the device based on the comparison.

Claims

1. A system, comprising:

a model-based control system comprising a processor, the processor configured to;

receive data relating to one or more parameters of a machinery via a plurality of sensors coupled to the machinery;

select one or more models configured to generate a desired parameter of the machinery based on a determined relationship between the one or more parameters and the desired parameter, wherein the one or more models represent a performance of a device of the machinery;

generate the desired parameter using the data and the one or more models;

control a plurality of actuators coupled to the machinery based on the desired parameter;

empirically tune the one or more models based on the data, the one or more parameters, and the desired parameter;

compare the empirical tuning to a baseline tuning, wherein the baseline tuning comprises determining a preferred value of the desired parameter in a particular operation condition of the machinery;

determine an operational state of the device based on the comparison; and

provide the operational state of the device to a user of the device.

2. The system of claim 1, wherein the one or more parameters comprise one or more surrogate parameters.

3. The system of claim 1, wherein empirically tuning the one or more models comprises performing regression analysis.

4. The system of claim 1, wherein the baseline tuning comprises a model, a set of coefficients derived during an empirical tuning, or any combination thereof.

5. The system of claim 1, wherein the processor is configured to store the empirically tuned one or more models, a set of coefficients derived during the empirical tuning, or any combination thereof as the baseline tuning.

6. The system of claim 1, wherein the processor is configured to store the empirical tuning and the baseline tuning, and use the stored empirical tuning and baseline tuning to predict a future operational state of the device.

7. The system of claim 6, wherein the processor is configured to determine a suggested replacement date for the device.

8. A method, comprising:

receiving, via a processor included in a control system, data from a plurality of sensors coupled to machinery configured to produce power, the data corresponding to one or more parameters of the machinery;

selecting, via the processor, one or more models configured to generate a desired parameter of the machinery based on a determined relationship between the one or more parameters and the desired parameter, the one or more models representing a performance of a device of the machinery;

generating, via the processor, the desired parameter using the data and the one or more models;

performing, via the processor, regression analysis to update the one or more models using the data, the one or more models, and the desired parameter, wherein performing the regression analysis comprises calculating one or more coefficients;

comparing, via the processor, the updated one or more models, the one or more coefficients, or any combination thereof to a baseline tuning, wherein the baseline tuning comprises determining a preferred value of the desired parameter in a particular operation condition of the machinery;

determining, via the processor, an operational state of the device based on the comparison; and

providing the operational state of the device to a user of the device.

9. The method of claim 8, comprising using one or more surrogate parameters as the one or more parameters.

10. The method of claim 8, comprising adjusting, via the processor, the baseline tuning based on a result of the regression analysis.

11. The method of claim 8, comprising predicting, via the processor, a future operational state of the device based, at least in part, on the comparison.

12. The method of claim 8, wherein the baseline tuning comprises a model, a set of coefficients derived during an empirical tuning, or any combination thereof.

13. The method of claim 8, comprising storing, via the processor, a result of the regression analysis, the baseline tuning, or any combination thereof and performing a historical analysis based on, at least in part, the stored result and the stored baseline tuning.

14. The method of claim 8, comprising determining, via the processor, whether a portion of the received data is unsuitable and discarding the unsuitable portion.

15. A non-transitory, computer-readable medium comprising computer executable code comprising instructions configured to:

receive, via a processor included in a control system, one or more parameters of a machinery configured to produce power, one or more desired parameters of the machinery, and one or more models that represent a determined relationship between the one or more parameters and the one or more desired parameters and a performance of one or more devices of the machinery;

perform, via the processor, regression analysis to determine one or more coefficients to apply to the one or more models and adjust the one or more models using the one or more parameters, the one or more desired parameters, and the one or more models;

compare, via the processor, the one or more adjusted models, the one or more coefficients, or any combination thereof to a baseline model, a baseline set of coefficients, or any combination thereof, wherein the baseline model comprises a baseline tuning comprising determining a preferred value of the one or more desired parameters in a particular operation condition of the machinery;

determine, via the processor, an operational state of the one or more devices based on the comparison;

adjust, via the processor, the baseline model, the baseline set of coefficients, or any combination thereof based on a result of the regression analysis;

store, via the processor, the adjusted one or more models, the one or more coefficients, the adjusted baseline model, the adjusted baseline set of coefficients, or any combination thereof; and

provide the operational state of the device to a user of the device.

16. The non-transitory, computer-readable medium of claim 15, comprising instructions configured to perform, via the processor, a historical analysis based, at least in part, on the stored adjusted one or more models, the stored one or more coefficients, the stored adjusted baseline model, the stored adjusted baseline set of coefficients, or any combination thereof.

17. The non-transitory, computer-readable medium of claim 16, comprising instructions configured to predict, via the processor, a future operational state of the one or more devices.

18. The non-transitory, computer-readable medium of claim 16, comprising instructions configured to determine, via the processor, a suggested replacement date for the one or more devices based on the historical analysis.

19. The non-transitory, computer-readable medium of claim 15, comprising instructions to use, via the processor, one or more surrogate parameters as the one or more parameters.

20. The non-transitory computer-readable medium of claim 15, comprising instructions configured to determine, via the processor, whether a portion of the one or more parameters is unsuitable and discard the unsuitable portion.