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.

Abstract: Reference data from sensors measuring characteristics of a gas turbine are analyzed to identify underperformance of the gas turbine, which may be a predictor of an unscheduled shutdown. Time series data from the sensors are compared to annotated query data using an open-begin-end dynamic time warping algorithm. Identified subsequences are examined as possible underperformance indicators. In a related technique, multiple time series from the sensors are pairwise compared using a dynamic time warping algorithm, and computed distances between the time series are used to group the time series using a hierarchical clustering algorithm. The clusters are examined to identify underperformance indicators.