Abstract: A monitoring system for a gas turbine includes a processor configured to receive an operating signal indicating an operating parameter of the gas turbine. The processor is configured to predict an occurrence of a lean blowout (LBO) event based on the operating parameter and an entropy ratio of combustion dynamics associated with a combustor of the gas turbine, wherein the LBO event corresponds to when the combustor stops firing. The processor is configured to send an alarm signal indicating the predicted LBO event to an electronic device prior to the occurrence of the LBO event.

Abstract: A monitoring system for a gas turbine includes a processor configured to receive an operating signal indicating an operating parameter of the gas turbine. The processor is configured to predict an occurrence of a lean blowout (LBO) event based on the operating parameter and an entropy ratio of combustion dynamics associated with a combustor of the gas turbine, wherein the LBO event corresponds to when the combustor stops firing. The processor is configured to send an alarm signal indicating the predicted LBO event to an electronic device prior to the occurrence of the LBO event.

Abstract: Systems and methods for gas turbine combustion profile monitoring are disclosed. In one embodiment, a method for detecting an anomaly in a combustion section of a gas turbine is disclosed. The method includes receiving, from a plurality of thermal sensors disposed around an exhaust section of a gas turbine, exhaust profile data of the gas turbine. The method further analyzes the exhaust profile data to calculate statistical features associated with a peak-trough pattern. The method further determines, using a machine learning algorithm, that the statistical features are abnormal. In response to the determination, the method processes the exhaust profile data for a predetermined period of time and reports an anomaly in a combustion section of the gas turbine if the statistical features remain abnormal for the predetermined period of time.

Abstract: A method of monitoring for combustion anomalies in a gas turbomachine includes sensing an exhaust gas temperature at each of a plurality of temperature sensors arranged in an exhaust system of the gas turbomachine, comparing the exhaust gas temperature at each of the plurality of temperature sensors with a mean exhaust gas temperature, determining whether the exhaust gas temperature at one or more of the plurality of temperature sensors deviates from the mean exhaust temperature by a predetermined threshold value, and identifying an instantaneous combustion anomaly at one or more of the temperature sensors sensing a temperature deviating from the mean exhaust temperature by more than the predetermined threshold value.

Abstract: Systems and methods for gas turbine combustion profile monitoring are disclosed. In one embodiment, a method for detecting an anomaly in a combustion section of a gas turbine is disclosed. The method includes receiving, from a plurality of thermal sensors disposed around an exhaust section of a gas turbine, exhaust profile data of the gas turbine. The method further analyzes the exhaust profile data to calculate statistical features associated with a peak-trough pattern. The method further determines, using a machine learning algorithm, that the statistical features are abnormal. In response to the determination, the method processes the exhaust profile data for a predetermined period of time and reports an anomaly in a combustion section of the gas turbine if the statistical features remain abnormal for the predetermined period of time.

Abstract: A method of monitoring for combustion anomalies in a gas turbomachine includes sensing an exhaust gas temperature at each of a plurality of temperature sensors arranged in an exhaust system of the gas turbomachine, comparing the exhaust gas temperature at each of the plurality of temperature sensors with a mean exhaust gas temperature, determining whether the exhaust gas temperature at one or more of the plurality of temperature sensors deviates from the mean exhaust temperature by a predetermined threshold value, and identifying an instantaneous combustion anomaly at one or more of the temperature sensors sensing a temperature deviating from the mean exhaust temperature by more than the predetermined threshold value.