Using an irrelevance filter to facilitate efficient RUL analyses for utility system assets

1. A method for estimating a remaining useful life (RUL) of a utility system asset, wherein during a surveillance mode, the method comprises iteratively performing the following operations:

receiving a set of present time-series signals gathered from sensors in the utility system asset;

using an inferential model to generate estimated values for the set of present time-series signals;

performing a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals;

performing a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms;

applying an irrelevance filter to the SPRT alarms to produce filtered SPRT alarms, wherein the irrelevance filter removes SPRT alarms for signals that are not correlated with previous failures of similar utility system assets;

using a logistic-regression model to compute an RUL-based risk index for the utility system asset based on the filtered SPRT alarms; and

when the risk index exceeds a risk-index threshold, generating a notification indicating that the utility system asset needs to be replaced.

2. The method of claim 1 , wherein the method further comprises periodically updating the logistic-regression model and the irrelevance filter based on time-series signals from additional utility system assets that have failed.

3. The method of claim 1 , wherein to reduce computational workload, the RUL-based risk index is computed for a utility system asset only when more than a threshold number of filtered SPRT alarms were generated during a preceding time interval.

4. The method of claim 1 , wherein during an inferential-training mode, which precedes the surveillance mode, the method comprises:

receiving an inferential training set of time-series signals gathered from sensors in the utility system asset during normal fault-free operation; and

training the inferential model to predict values of the time-series signals based on the inferential training set.

5. The method of claim 1 , wherein during an RUL-training mode, which precedes the surveillance mode, the method comprises:

receiving an RUL training set comprising time-series signals gathered from sensors in similar utility system assets while the similar utility system assets are run to failure;

receiving associated failure times for the similar utility system assets;

using the inferential model to generate estimated values for the RUL training set of time-series signals;

performing a pairwise differencing operation between actual values and the estimated values for the RUL training set of time-series signals to produce residuals;

performing a SPRT on the residuals to produce SPRT alarms with associated tripping frequencies; and

training the logistic-regression model to predict an RUL for the utility system asset based on correlations between the SPRT alarm tripping frequencies and the failure times for the similar utility system assets.

6. The method of claim 5 , wherein during the RUL-training mode, the method additionally configures the irrelevance filter by:

identifying relevant SPRT alarms that were generated during a time interval before a utility system asset failed; and

configuring the irrelevance filter to remove SPRT alarms that are not relevant.

7. The method of claim 6 , wherein while training the logistic-regression model to predict the RUL for the utility system asset, the method only considers SPRT alarm tripping frequencies associated with relevant SPRT alarms.

8. The method of claim 1 , wherein the time-series signals gathered from sensors in the utility system asset include signals specifying one or more of the following:

temperatures;

currents;

voltages;

resistances;

capacitances;

vibrations;

dissolved gas metrics;

cooling system parameters; and

control signals.

9. The method of claim 1 , wherein the inferential model comprises a Multivariate State Estimation Technique (MSET) model.

10. The method of claim 1 , wherein the utility system asset comprises a power transformer.

11. A non-transitory, computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for estimating a remaining useful life (RUL) of a utility system asset, wherein during a surveillance mode, the method comprises iteratively performing the following operations:

receiving a set of present time-series signals gathered from sensors in the utility system asset;

using an inferential model to generate estimated values for the set of present time-series signals;

performing a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals;

performing a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms;

applying an irrelevance filter to the SPRT alarms to produce filtered SPRT alarms, wherein the irrelevance filter removes SPRT alarms for signals that are not correlated with previous failures of similar utility system assets;

using a logistic-regression model to compute an RUL-based risk index for the utility system asset based on the filtered SPRT alarms; and

when the risk index exceeds a risk-index threshold, generating a notification indicating that the utility system asset needs to be replaced.

12. The non-transitory, computer-readable storage medium of claim 11 , wherein the method further comprises periodically updating the logistic-regression model and the irrelevance filter based on time-series signals from additional utility system assets that have failed.

13. The non-transitory, computer-readable storage medium of claim 11 , wherein to reduce computational workload, the RUL-based risk index is computed for a utility system asset only when more than a threshold number of filtered SPRT alarms were generated during a preceding time interval.

14. The non-transitory, computer-readable storage medium of claim 11 , wherein during an inferential-training mode, which precedes the surveillance mode, the method comprises:

receiving an inferential training set of time-series signals gathered from sensors in the utility system asset during normal fault-free operation; and

training the inferential model to predict values of the time-series signals based on the inferential training set.

15. The non-transitory, computer-readable storage medium of claim 11 , wherein during an RUL-training mode, which precedes the surveillance mode, the method comprises:

receiving an RUL training set comprising time-series signals gathered from sensors in similar utility system assets while the similar utility system assets are run to failure;

receiving associated failure times for the similar utility system assets;

using the inferential model to generate estimated values for the RUL training set of time-series signals;

performing a pairwise differencing operation between actual values and the estimated values for the RUL training set of time-series signals to produce residuals;

performing a SPRT on the residuals to produce SPRT alarms with associated tripping frequencies; and

training the logistic-regression model to predict an RUL for the utility system asset based on correlations between the SPRT alarm tripping frequencies and the failure times for the similar utility system assets.

16. The non-transitory, computer-readable storage medium of claim 15 , wherein during the RUL-training mode, the method additionally configures the irrelevance filter by:

identifying relevant SPRT alarms that were generated during a time interval before a utility system asset failed; and

configuring the irrelevance filter to remove SPRT alarms that are not relevant.

17. The non-transitory, computer-readable storage medium of claim 16 , wherein while training the logistic-regression model to predict the RUL for the utility system asset, the method only considers SPRT alarm tripping frequencies associated with relevant SPRT alarms.

18. A system that estimates a remaining useful life (RUL) of a utility system asset, comprising:

at least one processor and at least one associated memory; and

a notification mechanism that executes on the at least one processor, wherein during a surveillance mode, the notification mechanism iteratively:

receives a set of present time-series signals gathered from sensors in the utility system asset;

uses an inferential model to generate estimated values for the set of present time-series signals;

performs a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals;

performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms;

applies an irrelevance filter to the SPRT alarms to produce filtered SPRT alarms, wherein the irrelevance filter removes SPRT alarms for signals that are not correlated with previous failures of similar utility system assets;

uses a logistic-regression model to compute an RUL-based risk index for the utility system asset based on the filtered SPRT alarms; and

when the risk index exceeds a risk-index threshold, generates a notification indicating that the utility system asset needs to be replaced.

19. The system of claim 18 , wherein during an RUL-training mode, which precedes the surveillance mode, the notification mechanism:

receives an RUL training set comprising time-series signals gathered from sensors in similar utility system assets while the similar utility system assets are run to failure;

receives associated failure times for the similar utility system assets;

using the inferential model to generate estimated values for the RUL training set of time-series signals;

performs a pairwise differencing operation between actual values and the estimated values for the RUL training set of time-series signals to produce residuals;

performs a SPRT on the residuals to produce SPRT alarms with associated tripping frequencies; and

trains the logistic-regression model to predict an RUL for the utility system asset based on correlations between the SPRT alarm tripping frequencies and the failure times for the similar utility system assets.

20. The system of claim 19 , wherein during the RUL-training mode, the notification mechanism additionally configures the irrelevance filter, wherein during this configuration process the notification mechanism:

identifies relevant SPRT alarms that were generated during a time interval before a utility system asset failed; and

configures the irrelevance filter to remove SPRT alarms that are not relevant.