Adaptive model training system and method

• US 8,700,550 B1
• Filed: 03/29/2010
• Issued: 04/15/2014
• Est. Priority Date: 11/30/2007
• Status: Active Grant

• Current Assignee: Intellectual Assets LLC
• Original Assignee: Intellectual Assets LLC
• Inventors: Bickford, Randall L., Palnitkar, Rahul M., Lee, Vo
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Sugent, James F
• Application Number:
  US12/798,152
• Time in Patent Office: 1,478 Days
• Field of Search: 706/14, 702/85, 700/286
• US Class Current: 706/14
• CPC Class Codes: G01K 15/005 Calibration
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01R 35/005 Calibrating; Standards or reference devices, e.g. voltage or resistance standards, "golden" references G01R33/0035, G01R35/002 take precedence
• CPC Class Codes: G05B 13/02 electric
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/004 Artificial life, i.e. computing arrangements simulating life
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks

Abstract

An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

Claims

1. A computer-implemented adaptive model training method, said method comprising the steps of:

providing a previously trained model having a learned scope of normal operation of an asset obtained from an initial set of training data values;

acquiring a set of asset operating data values from the asset for defining operation of the asset;

assigning a measure of data quality to the asset operating data values in the acquired set of asset operating data values based on at least one predefined criterion for comparing the asset operating data values in the acquired set of asset operating data values with the previously trained model having the learned scope of normal operation of the asset;

filtering the acquired set of asset operating data values for selecting an additional set of training data values from the acquired set of asset operating data values based on at least one predefined criterion of good data quality utilizing the measure of data quality assigned to the asset operating data values in the acquired set of asset operating data values;

creating an adapted set of training data values for defining an adapted scope of normal operation of the asset by combining at least one of the data values from the initial set of training data values with at least one of the data values from the selected additional set of training data values based on at least one predefined criterion for selectively choosing the data values included in the adapted set of training data values; and

recalibrating the previously trained model having the learned scope of normal operation of the asset by utilizing the created adapted set of training data values for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

2. The computer-implemented method of claim 1 further comprising the steps of:

subsequently filtering subsequently acquired asset operating data values from the asset for selectively choosing subsequently acquired asset operating data values that meet at least the one predefined criterion of good data quality while rejecting subsequently acquired asset operating data values that fail to meet at least the one predefined criterion of good data quality; and

subsequently recalibrating the recalibrated model having the adjusted learned scope of normal operation of the asset by utilizing the subsequently acquired asset operating data values that meet at least the one predefined criterion of good data quality for subsequently adjusting the learned scope of normal operation of the asset.

3. The computer-implemented method of claim 2 further comprising a step of iteratively repeating the subsequently filtering step followed by the subsequently recalibrating step as a function of a user-specified period.

4. The computer-implemented method of claim 2 further comprising a step of iteratively repeating the subsequently filtering step followed by the subsequently recalibrating step as a function of user demand.

5. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform an adaptive model training method, said method comprising:

providing a previously trained model having a learned scope of normal operation of an asset obtained from an initial set of training data values;

acquiring a set of asset operating data values from the asset for defining operation of the asset;

assigning a measure of data quality to the asset operating data values in the acquired set of asset operating data values based on at least one predefined criterion for comparing the asset operating data values in the acquired set of asset operating data values with the previously trained model having the learned scope of normal operation of the asset;

filtering the acquired set of asset operating data values for selecting an additional set of training data values from the acquired set of asset operating data values based on at least one predefined criterion of good data quality utilizing the measure of data quality assigned to the asset operating data values in the acquired set of asset operating data values;

creating an adapted set of training data values for defining an adapted scope of normal operation of the asset by combining at least one of the data values from the initial set of training data values with at least one of the data values from the selected additional set of training data values based on at least one predefined criterion for selectively choosing the data values included in the adapted set of training data values; and

recalibrating the previously trained model having the learned scope of normal operation of the asset by utilizing the created adapted set of training data values for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

6. The non-transitory computer-readable medium of claim 5 further comprising the steps of:

subsequently filtering subsequently acquired asset operating data values from the asset for selectively choosing subsequently acquired asset operating data values that meet at least the one predefined criterion of good data quality while rejecting subsequently acquired asset operating data values that fail to meet at least the one predefined criterion of good data quality; and

subsequently recalibrating the recalibrated model having the adjusted learned scope of normal operation of the asset by utilizing the subsequently acquired asset operating data values that meet at least the one predefined criterion of good data quality for subsequently adjusting the learned scope of normal operation of the asset.

7. The non-transitory computer-readable medium of claim 6 further comprising a step of iteratively repeating the subsequently filtering step followed by the subsequently recalibrating step as a function of a user-specified period.

8. The non-transitory computer-readable medium of claim 6 further comprising a step of iteratively repeating the subsequently filtering step followed by the subsequently recalibrating step as a function of user demand.

9. An adaptive model training system, said system comprising:

a previously trained model stored in a non-transitory computer-readable medium, said previously trained model having a learned scope of normal operation of the asset;

means for acquiring a set of asset operating data values from the asset for defining operation of the asset;

assigning a measure of data quality to the asset operating data values in the acquired set of asset operating data values based on at least one predefined criterion for comparing the asset operating data values in the acquired set of asset operating data values with the previously trained model having the learned scope of normal operation of the asset;

means for filtering the acquired set of asset operating data values for selecting an additional set of training data values from the acquired set of asset operating data values based on at least one predefined criterion of good data quality utilizing the measure of data quality assigned to the asset operating data values in the acquired set of asset operating data values;

means for creating an adapted set of training data values for defining an adapted scope of normal operation of the asset by combining at least one of the data values from the initial set of training data values with at least one of the data values from the selected additional set of training data values based on at least one predefined criterion for selectively choosing the data values included in the adapted set of training data values; and

means for recalibrating the previously trained model having the learned scope of normal operation of the asset by utilizing the created adapted set of training data values for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

10. The system of claim 9 further comprising:

means for subsequently filtering subsequently acquired asset operating data values from the asset for selectively choosing subsequently acquired asset operating data values that meet at least the one predefined criterion of good data quality while rejecting subsequently acquired asset operating data values that fail to meet at least said one predefined criterion of good data quality; and

means for subsequently recalibrating said recalibrated model having said learned scope of normal operation of the asset by utilizing the subsequently acquired asset operating data values that meet at least said one predefined criterion of good data quality for readjusting said learned scope of normal operation of the asset for defining a subsequently recalibrated model having said readjusted learned scope of normal operation of the asset.

11. The system of claim 9 further comprising means for iteratively repeating said subsequently filtering function followed by the subsequently recalibrating function based on a user-specified period.

12. The system of claim 9 further comprising means for iteratively repeating said subsequently filtering function followed by said subsequently recalibrating function based on user demand.

13. A computer-implemented adaptive model training method, said method comprising the steps of:

providing an initial set of training data values;

calibrating a model for defining an initial scope of normal operation of an asset using the provided initial set of training data values;

acquiring an additional set of data values from the asset for defining operation of the asset;

assigning a measure of data quality to the data values in the acquired additional set of data values based on at least one predefined criterion for comparing the data values in the acquired additional set of data values with the model for defining the scope of normal operation of the asset;

selecting an additional set of training data values from the acquired additional set of data values based on at least one predefined criterion of good data quality using the measure of data quality assigned to the data values in the acquired additional set of data values;

creating an adapted set of training data values for defining an adapted scope of normal operation of the asset by combining at least one of data values from the provided initial set of training data values with at least one of the data values from the selected additional set of training data values based on at least one predefined criterion for selectively choosing the data values included in the adapted set of training data values; and

recalibrating the model for defining the scope of normal operation of the asset using the created adapted set of data values for defining a recalibrated model having an adjusted scope of normal operation of the asset.

14. The computer-implemented method of claim 13 further comprising the steps of:

acquiring a further set of data values for defining an operation of the asset;

assigning a measure of data quality to the data values in the acquired further set of data values based on at least one predefined criterion for comparing the data values in the acquired further set of data values with the recalibrated model for defining the scope of normal operation of the asset;

selecting a further set of training data values from the acquired further set of data values based on at least one predefined criterion using the measure of data quality assigned to the data values in the acquired further set of data values;

creating a further adapted set of training data values for defining a further adapted scope of normal operation of the asset by combining at least one of data values from the created adapted set of data values with at least one of the data values from the selected further set of training data values based on at least one predefined criterion for selectively choosing the data values included in the further adapted set of training data values;

recalibrating the recalibrated model for defining the scope of normal operation of the asset using the created further adapted set of training data values.

15. The computer-implemented method of claim 13 wherein the step of assigning a measure of data quality to the data values in the acquired additional set of data values includes the steps of:

estimating the expected value of at least one data value in the acquired additional set of data values using the model for defining the scope of normal operation of the asset;

comparing at least one data value in the acquired additional set of data values with the estimated expected value of at least one data value in the acquired additional set of data values;

assigning a measure of data quality to the data values in the acquired additional set of data values based on the comparing step.