General Electric Company

Saikat Ray Majumder

Subhrajit Roychowdhury

Harry Kirk Mathews, Jr

Thomas Graham Spears

Sarah Felix

Lalit Keshav Mestha

Masoud Abbaszadeh

Weizhong Yan

Naresh Sundaram Iyer

Sumit Bose

Chaitanya Ashok Baone

Xiaohu Ping

Olugbenga Moses Anubi

Santosh Sambamoorthy Veda

Harry Kirk Matthews, Jr

Annartia Giani

Harry Kirk Mathews

An additive manufacturing apparatus, a computing system, and a method for operating an additive manufacturing apparatus are provided. The method includes obtaining two or more images corresponding to respective build layers at a build plate, wherein each image comprises a plurality of data points comprising a feature and corresponding location at the build plate; removing variation between the features of the plurality of data points; and normalizing each feature to remove location dependence in the plurality of data points.

System and method for additive manufacturing control

Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

System and methods for generating fault indications for an additive manufacturing process based on a probabilistic comparison of the outputs of multiple process models to measured sensor data

According to some embodiments, a plurality of heterogeneous data source nodes may each generate a series of current data source node values over time that represent a current operation of an electric power grid. A real-time threat detection computer, coupled to the plurality of heterogeneous data source nodes, may receive the series of current data source node values and generate a set of current feature vectors. The threat detection computer may then access an abnormal state detection model having at least one decision boundary created offline using at least one of normal and abnormal feature vectors. The abnormal state detection model may be executed, and a threat alert signal may be transmitted if appropriate based on the set of current feature vectors and the at least one decision boundary.

Generic framework to detect cyber threats in electric power grid

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Niskayuna, NY, United States of America

Saikat Ray Majumder