General Electric Company

John Robert Korsedal

Awadesh Kumar Tiwari

Yan Zhao

Rebecca Evelyn Hefner

Joseph Leonard Moroso

Xiaomo Jiang

Christopher Dean Higgins

Carlos Andres Hein

Jenfu Yau

Anshuman Singh

Christopher Farral

Marc Gavin Lindenmuth

Luis Armando Sanchez Del Valle

Ryan Adam Conger

Bradford Foulkes

David Purnell

Shridhar Raghuvir Powar

Philip Lee Schoonover, Ii

Jose Maria Gurria Llorente

Kowta Ramshanker

Molli Anne Dill

Jianxiong Chen

Shirish Kumar Pandey

Seema Chopra

Truman Blake Horner

Embodiments of the present disclosure include a method for controlling a power generation system, the method including: detecting a heat distribution across a component of a power generation system from a thermal output of the component, during operation of the power generation system; calculating a projected heat distribution across the component based on a library of modeling data for the power generation system; calculating whether a difference between the heat distribution and the projected heat distribution exceeds a thermal threshold; adjusting the power generation system in response to the difference exceeding the predetermined threshold, wherein the adjusting includes modifying an operating setting of the power generation system.

Control of power generation system by visually monitoring component during operation

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

This disclosure provides systems for using failure mode ranking in an asset management system to generate asset maintenance outputs. Operational data, failure mode models, and configuration data for an asset, such as a complex electromechanical system, are related to failure prevention analytics configurations through failure mode rankings to enable the asset management system to reduce a future ranking of potential failure modes by changing the present configuration of the asset to include a recommended failure prevention analytics configuration.

Failure mode ranking in an asset management system

A power plant system includes a turbine system, a sensor, and a controller. The sensor is configured to collect a first set of data regarding the turbine system. The controller is configured to receive user input regarding constraints of the turbine system, a second set of data regarding the power plant system, and the first set of data. The controller is configured to determine whether a first notification is present based on a determined status and to generate a first maintenance package based on the first notification, life odometer solutions, condition monitoring solutions, and the first set of data. The controller is configured to generate a model of implementing the first maintenance package with respect to the turbine system as well as to generate a second maintenance package based on the user input, the second set of data, the first maintenance package, and the scenario model.

Systems and methods for generating maintenance packages

A system includes a controller configured to control an operation of a turbine system, and an analytics system coupled to the controller and configured to receive inputs corresponding to the operation of the turbine system, generate an operational impact factor (OIF) value based at least in part on the inputs, generate a turbine system life prediction model configured to predict an operating life of one or more components of the turbine system based at least in part on the OIF value, and provide the OIF value to the controller to perform an action based thereon.

Systems and methods for determining operational impact on turbine component creep life

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

Systems and methods for gas turbine operational impact modeling using statistical and physics-based methodologies are disclosed. According to one embodiment of the disclosure, a method can include receiving, by one or more processors, operational conditions data associated with a hardware component of a gas turbine; based at least in part on the operational variation data, applying, by one or more processors, statistical methods to establish an operational profile of the hardware component; receiving, by one or more processors, operating parameters and operational conditions data associated with the hardware component; based at least in part on the operating parameters and the operational conditions data, applying, by one or more processors, physics-based methods to establish an operational impact factor of the hardware component; and based at least in part on the operational profile and the operational impact factor determining, by one or more processors, a probability of a failure of the hardware component within a time period.

Systems and methods for gas turbine operational impact modeling using statistical and physics-based methodologies

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Greenville, SC, United States of America

John Robert Korsedal