General Electric Company

Lijie Yu

Karen Warren Miller

Robert Joseph Iasillo

Venkatesh Rajagopalan

Sachin Srivastava

Xiaomo Jiang

Devang Jagdish Gandhi

Matthew Everett Moore

Ilan Leibu

Mayank Lalan

Ketan Kalele

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.

System and method to enhance lean blowout monitoring

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.

Gas turbine combustion profile monitoring

Systems and methods for blade health monitoring are provided. According to one embodiment of the disclosure, a system may include a feature extraction module and an anomaly detection module in communication with the extraction module. The feature extraction module may be configured to continuously receive blade passing signal data associated with clearance of a blade and pre-process the blade passing signal data. Blade clearance feature data may be extracted from the blade passing signal data prior to transmission to the anomaly detection module. The anomaly detection module may be configured to normalize the blade clearance feature data received from the extraction module, analyze the blade clearance feature data to detect a shift in the clearance of the blade, and determine an abnormality of the blade based on the shift exceeding a predetermined shift threshold.

Methods and systems for blade health monitoring

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Atlanta, GA, United States of America

Lijie Yu