Abstract: A method and system for recognizing (and/or predicting) failures of sensors used in monitoring gas turbines applies a sparse coding process to collected sensor readings and defines the L-1 norm residuals from the sparse coding process as indicative of a potential sensor problem. Further evaluation of the group of residual sensor readings is perform to categorize the group and determine if there are significant outliers (“abnormal data”), which would be considered as more likely associated with a faulty sensor than noisy data. A time component is introduced into the evaluation that compares a current abnormal result with a set of prior results and making the faulty sensor determination if a significant number of prior readings also have an abnormal value. By taking the time component into consideration, the number of false positives is reduced.

Abstract: A method and system for recognizing (and/or predicting) failures of sensors used in monitoring gas turbines applies a sparse coding process to collected sensor readings and defines the L-1 norm residuals from the sparse coding process as indicative of a potential sensor problem. Further evaluation of the group of residual sensor readings is perform to categorize the group and determine if there are significant outliers (“abnormal data”), which would be considered as more likely associated with a faulty sensor than noisy data. A time component is introduced into the evaluation that compares a current abnormal result with a set of prior results and making the faulty sensor determination if a significant number of prior readings also have an abnormal value. By taking the time component into consideration, the number of false positives is reduced.

Abstract: A system and method for predicting failures of machinery such as a gas turbine. The system and method utilizes computer-based system to annotate historical data locate a prior failure event. Data associated with sensor readings prior to the failure event is annotated to note that it is likely associated with a failure and is compared to normal operating condition data. A fast boxes algorithm is used to learn the location of the pre-event data (positive class, minority group) with respect to the normal operation data (negative class, majority group). An evaluation is performed to analyze the discriminatory strength of the pre-event data with respect to the normal data, and if a relatively strong difference is found, the associated pre-event data is stored and used as a “symptom” to monitor the on-going performance of a machine and predict the possibility of an unexpected failure days before it would otherwise occur.