System, device, and methods for updating system-monitoring models

1. An electronically-implemented model association method for updating estimation models used for system monitoring, the method comprising:

for each of a plurality of monitored systems, determining an association between a particular monitored system and at least one of a plurality of estimation models, wherein each estimation model is based upon one of a plurality of distinct sets of estimation properties, each set of estimation properties including at least one among a sensor list, sensor thresholds, training period, and estimation model algorithm, and wherein each set uniquely corresponds to a particular estimation model;

updating at least one of the estimation properties;

propagating the at least one updated estimation property to each estimation model that corresponds to a distinct set containing the at least one estimation property that is updated;

modifying each estimation model that corresponds to a distinct set containing the at least one estimation property that is updated;

updating an association between one or more estimation models and at least one of the plurality of monitored systems in response to a structure of at least one monitored system changing; and

retaining a system-specific version of each estimation model that is modified.

2. The method of claim 1 , further comprising training a system-specific version of each estimation model for each monitored system, wherein the training is based upon sensor data generated by sensors communicatively linked to a particular system for which a particular system-specific version of an estimation model is trained.

3. The method of claim 2 , further comprising retraining each system-specific version of each estimation model that is modified, wherein the retraining is based upon sensor data generated by sensors communicatively linked to a particular system for which a particular system-specific version of an estimation model is retrained.

4. The method of claim 3 , wherein retraining is based upon a least-squares estimation.

5. The method of claim 1 , further comprising generating a fleet table to represent the associations between the plurality of monitored systems and the plurality of estimation models.

6. The method of claim 5 , wherein the fleet table comprises an M×N matrix, M being an integer greater than one comprising a number of monitored systems, and N being an integer equal to a number of estimation models.

7. A system for updating a plurality of monitoring models, the system comprising:

a model association module that for each of a plurality of monitored systems determines an association between a particular monitored system and at least one of a plurality of estimation models, wherein each estimation model is based upon one of a plurality of distinct sets of estimation properties, each set of estimation properties including at least one among a sensor list, sensor thresholds, training period, and estimation model algorithm, and wherein each set uniquely corresponds to a particular estimation model;

an updating module that updates at least one of the estimation properties and propagates the at least one updated estimation property to each estimation model that corresponds to a distinct set containing the at least one estimation property that is updated; and

a model modification module that modifies each estimation model that corresponds to a distinct set containing the at least one estimation property that is updated;

the model modification module is configured to update an association between one or more estimation models and at least one of the plurality of monitored systems in response to a structure of at least one monitored system changing, and to cause a system-specific version of each estimation model that is modified to be electronically stored.

8. The system of claim 7 , further comprising a training module configured to train a system-specific version of each estimation model for each monitored system based upon sensor data generated by sensors communicatively linked to a particular system for which a particular system-specific version of an estimation model is trained.

9. The system of claim 8 , wherein the training module is further configured to retrain each system-specific version of each estimation model that is modified.

10. The system of claim 7 , wherein the system is deployed at a location remote from the plurality of monitored systems.

11. A computer-readable storage medium comprising computer instructions for:

determining for each of a plurality of monitored systems an association between a particular monitored system and at least one of a plurality of estimation models, wherein each estimation model is based upon one of a plurality of distinct sets of estimation properties, each set of estimation properties including at least one among a sensor list, sensor thresholds, training period, and estimation model algorithm, and wherein each set uniquely corresponds to a particular estimation model;

updating at least one of the estimation properties;

propagating at least one updated estimation property to each estimation model that corresponds to a distinct set containing the at least one estimation property that is updated;

modifying each estimation model that corresponds to a distinct set containing the at least one estimation property that is updated;

updating an association between one or more estimation models and at least one of the plurality of monitored systems in response to a structure of at least one monitored system changing; and

retaining a system-specific version of each estimation model that is modified.

12. The computer-readable storage medium of claim 11 , further comprising computer instructions for training a system-specific version of each estimation model for each monitored system, wherein the training is based upon sensor data generated by sensors communicatively linked to a particular system for which a particular system-specific version of an estimation model is trained.

13. The computer-readable storage medium of claim 12 , further comprising computer instructions for retraining each system-specific version of each estimation model that is modified, wherein the retraining is based upon sensor data generated by sensors communicatively linked to a particular system for which a particular system-specific version of an estimation model is retrained.

14. The computer-readable storage medium of claim 13 , wherein the computer instructions for refraining comprise instructions for performing estimations based upon a least-squares method.