General Electric Company

Vasanth Muralidharan

Srikanth Akkaram

Sulficker Ali

Andrew Scott Kessie

Jitendra Kumar

Charles Larry Abernathy

Michael Howard Fisher

Gregory Jon Chiaramonte

Hemanth Gudibande Sathyakumar Kumar

Abhay Sudhakarrao Kant

Yan Chen

Youngwon Shin

Amarnath Kakarla

Ricardo Cuevas

S N Arul Saravanapriyan

William Keith Kincaid

Mahalakshmi Balasubramaniam S B

Jesus Miguel Valenzuela

Trevor A Tersmette

Steven Laverne Johann

Karan Oberoi

Venkata Vamsi Bhagavan

Rebinth Robin

Mariusz Wiklo

A system and method for monitoring and diagnosis of engine health are provided. In one aspect, a system receives continuous operating data (COD) associated with an asset. The COD includes parameter values for one or more parameters over a collection time period. The system generates synthetic snapshot data based at least in part on the COD. The synthetic snapshot data includes one or more synthetic snapshots each containing the parameter values for the one or more parameters for a given timepoint within the collection time period. The system also receives snapshot data associated with the asset. The snapshot data includes one or more snapshots each containing parameter values for the one or more parameters for a given timepoint. The system generates an output indicating a health status of the asset or one or more components thereof based at least in part on the snapshot data and the synthetic snapshot data.

System and method for monitoring and diagnosis of engine health using a snapshot-CEOD based approach

An apparatus and method for predicting distress on a physical component. The method can include obtaining distress data. The distress data can be used to determine a distress rank. The distress rank can be compared to a distress output provided by a kernel that use parameters related to the physical component. The comparison can result in a prediction model for the physical component.

Model for predicting distress on a component

Methods, apparatus, systems, and articles of manufacture are disclosed to generate a predictive asset health quantifier of a turbine engine. An example apparatus includes a performance model analyzer to determine a fleet behavior parameter by generating a reference performance model using historical information for a fleet of operators using turbine engines, generate a residual performance model based on calculating a difference between the fleet behavior parameter and a plurality of operator behavior parameters, identify an operator as a candidate improvement target based on comparing the operator behavior parameters corresponding to the operator to the fleet in the residual performance model, and determine an adjusted operator behavior parameter for the candidate improvement target. The example apparatus further includes a system updater to update a computer-based model to replace the operator behavior parameter with the adjusted operator behavior parameter, and a task optimizer to determine a workscope for the turbine engine.

Methods and apparatus to generate a predictive asset health quantifier of a turbine engine

Systems for thermally regulating portions of a steam turbine are disclosed. In one embodiment, a thermal control system for a rotor bearing support includes: a housing fluidly connected to an inlet and adapted to substantially enclose the rotor bearing support, the housing defining a first annular cavity adapted to receive a fluid from the inlet; and an outlet fluidly connected to the housing, the outlet adapted to receive the fluid from the annular cavity.

Rotor support thermal control system

A turbomachine rotor includes: a rotor body having an outer surface; and a patterned abrasive or abradable coating formed over the outer surface of the rotor body, the patterned abrasive or abradable coating for directing a flow of a working fluid across the turbomachine rotor.

Turbomachine rotor having patterned coating

A method to determine eccentricity of a rotor in a turbine including: collecting sensor data of rotor eccentricity for a plurality of startup operations; establishing a baseline eccentricity value using the sensor data corresponding to a selected startup operation; determining an eccentricity value using the filtered sensor data for each of a plurality of startup operations subsequent to the selected startup operation; determining a rotor eccentricity difference between the baseline eccentricity value and each of the eccentricity values for the plurality of startup operations subsequent to the selected startup operation, and reporting a rotor eccentricity condition based on the rotor eccentricity difference.

System and method for detection of rotor eccentricity baseline shift

Growing community of inventors

Bengaluru, India

Vasanth Muralidharan