General Electric Company

Xiaomo Jiang

John Robert Korsedal

Devang Jagdish Gandhi

Michael Edward Bernard

Awadesh Kumar Tiwari

Rebecca Evelyn Hefner

Sanjeev Shyam Heda

Sameer Vittal

Eric Gebhardt

Bradley Wile

Christopher Farral

Steven Hartman

Ilan Leibu

Lijie Yu

Karen Warren Miller

Matthew Everett Moore

Tong Zou

Bradford Foulkes

Kevin Clement Metz

James John D'Amato

Shirish Kumar Pandey

Jianxiong Chen

Kowta Ramshanker

Ketan Kalele

Embodiments of the disclosure can relate to providing an integrated power plant advisor. In one embodiment, a method for providing an integrated power plant advisor can include receiving a signal associated with a failure of a power plant or a power plant component. The method can further include determining one or more root causes associated with the failure of the power plant or the power plant component. Based at least in part on operational data and training data from one or more power plants, a ranking of the one or more root causes associated with the failure of the power plant or the power plant component can be determined. The method can further include outputting the ranking via a client device. Based at least in part on the ranking, a repair or replacement strategy for the power plant or the power plant component can be identified.

Systems and methods for providing an integrated power plant advisor

A system is provided comprising a memory configured to store instructions and a processor configured to execute the instructions. The processor is configured to execute the instructions to receive sensor data comprising sensed operations for a machinery, the sensed operations sensed via one or more sensors disposed in the machinery, and to derive a first model matrix based on the sensor data. The processor is further configured to derive a covariance regression model based on the first model matrix, wherein the covariance regression model is configured to be executed to derive a predictive event based on operational machinery data as input.

System and method for issue detection of industrial processes

A control system for a gas turbine includes a processor. The processor configured to access one or more operating parameters of the gas turbine. The operating parameters are configured to specify how the gas turbine operates. The processor is configured to predict a rate of degradation to one or more parts of a compressor of the gas turbine due to one or more effects on the parts by operating the gas turbine according to the one or more operating parameters. The processor is configured to send an alert to an electronic device based at least in part on the rate of degradation of the compressor.

System and method to enhance corrosion turbine monitoring

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 fault prediction are described to reduce equipment failure by effectively monitoring equipment, removing anomalous data, and reducing false alarms. Such systems and methods can be used to receive monitoring data, extract information from the data, and combine extracted information for establishing prediction models. Additionally, fault probabilities may be quantified and faults may be predicted based on the probabilities.

Fault prediction of monitored assets

Systems and methods are disclosed herein for enhancing turbomachine operations. Such systems and methods include a hybrid risk model. The hybrid risk model includes a physics-based sub model and a statistical sub model. The physics-based sub model is configured to model physical components of a turbomachine. The statistical sub model is configured to model historical information of the turbomachine. The hybrid risk model is configured to calculate a turbomachine parameter.

System and method for hybrid risk modeling of turbomachinery

A system for monitoring a gas turbine includes a memory containing information from comparable gas turbines and an input device that generates a unit data signal and a risk signal. A processor in communication with the memory and the input device incorporates the unit data signal into the database, projects information for the gas turbine, and calculates a conditional risk that the gas turbine will reach a limit. An output signal includes repair or maintenance schedules. A method for monitoring a gas turbine includes receiving information from comparable gas turbines, adding information from the gas turbine to the information from comparable gas turbines, and projecting information for the gas turbine. The method further includes calculating a conditional risk that the gas turbine will reach a limit and generating an output signal containing repair or maintenance schedules.

System and method for monitoring a gas turbine

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Atlanta, GA, United States of America

Xiaomo Jiang