General Electric Company

Ge Inspection Technologies, Gmbh

Ge Sensing & Inspection Technologies, Gmbh

Other

Baker Hughes Corporation

Arun Karthi Subramaniyan

Alexandre Nikolov Iankoulski

Ananda Barua

Daniel Jason Erno

Darren Lee Hallman

Changjie Sun

Natarajan Chennimalai Kumar

Liping Wang

John Lazos

Renato Giorgiani Do Nascimento

Shyam Sivaramakrishnan

Fabio Nonato De Paula

Felipe Antonio Chegury Viana

Haiming Zhao

Isaac Mendel Asher

You Ling

Ankur Srivastava

Ali Can

Clifford Bueno

Arvind Rangarajan

Jie Yu

Sathyanarayanan Raghavan

Shesh Krishna Srivatsa

Shourya Prakash Otta

Khan Mohamed Khirullah Genghis Khan

Adrian Gabriel Loghin

Albert Rosario Cerrone

Robert Munda

Nimrod Ohad

Don Beeson

Michael Kennedy

Mahadevan Balasubramaniam

Michael Szatny

Raj Ramalingam

Xuan Zhu

Felipe Viana

Imran Younus

Zhengjie Xu

An industrial asset item definition data store may contain at least one electronic record defining the industrial asset item. An automated support structure creation platform may include a support structure optimization computer processor. The automated support structure optimization computer processor may be adapted to automatically create support structure geometry data associated with an additive printing process for the industrial asset item. The creation may be performed via an iterative loop between a build process simulation engine and a topology optimization engine.

Optimizing support structures for additive manufacturing

A method includes receiving a first time-dependent data characterizing measurement by a first sensor operatively coupled to an oil and gas industrial machine; determining a first anomaly score associated with a first portion of the first time-dependent data over a time period, the determination is based on a first value of an operating characteristic over the time period and a second value of the operating characteristic over the time period, wherein the first value of the operating characteristic is calculated from the first time-dependent data and the second value of the operating characteristic is detected at the oil and gas industrial machine; and rendering, in a graphical user interface display space, a visual representation indicative of the first anomaly score. Related apparatus, systems, articles, and techniques are also described.

Anomaly detection

According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.

Framework for rapid additive design with generative techniques

Data characterizing a load on a rod of a down-hole pump at different positions of a stroke of the rod can be received. An image characterizing the load of the rod at the different positions of the stroke can be determined. A defect in operating conditions of the down-hole pump can be determined. The determining can include comparing the image to a set of predetermined images with associated defects. The determined defect can be provided.

Pattern matching to detect defects in rod lift downhole cards

Sensor data is received characterizing operational data associated with a machine. A data analytic specification is received from a user. The received operational data can be processed using the data analytic. A visual representation of the processed operational data can be rendered, within a graphical user interface display space. A digital model characterizing the machine can be generated based on user input received in the graphical user interface. Additional sensor data can be received. The digital model can be updated based on the received additional sensor data. A recommendation for modifying an input value and/or an operational parameter of the machine can be determined using the updated model. The recommendation can be rendered within the graphical user interface space. Related apparatus, systems, techniques and articles are also described.

Analytics engine

Systems, methods, and a computer readable medium are provided for generating natural language recommendations based on an industrial language model. Input data, including a plurality of natural language input units is received from a first computing device and transmitted to a second computing device. The second computing device can determine an output including a plurality of natural language outputs using a predictive model trained to generate the output corresponding to an energy exploration lexicon. The second computing device can provide the output to the first computing device for display.

Generating natural language recommendations based on an industrial language model

A unit cell structure is provided. The unit cell structure includes a first section and a second section. The first section defines a first load path and includes a first plurality of first unit cells joined together. The second section defines a second load path separate from the first load path and includes a second plurality of second unit cells joined together, each second unit cell of the second plurality of second unit cells nested within and spaced apart from each first unit cell of the first plurality of first unit cells of the first section.

Components including structures having decoupled load paths

A monitoring system can include a dashboard including a graphical user interface display space. The monitoring system can also include a digital twin validation (DTV) system. The DTV system can be configured to perform operations including rendering, in the graphical user interface display space, a visual representation of an output value of a digital model of an oil and gas industrial machine, and an interactive graphical object characterizing an input value of the digital model. The operations can also include receiving data characterizing user interaction with the interactive graphical object. The data characterizes the user interaction indicative of the input value of the digital model. The operations can also include updating the digital model to generate an updated output value representative of an operational characteristic of the oil and gas industrial machine. The operations can further include rendering, in the graphical user interface display space, a visual representation of the updated output value.

Oil and gas industrial machine monitoring

According to some embodiments, system and methods are provided comprising: receiving data; providing a simulation model for the data; generating one or more simulations via a Bayesian module based on the data, wherein the simulation includes one or more nodes in a chain; executing the Bayesian module to determine the acceptability of the nodes in the simulation based on a Bayesian rule, wherein execution of the Bayesian module further comprises: generating a binary decision tree representing the chain in the simulation, wherein the chain includes one or more nodes; prioritizing which nodes in the tree to simulate; generating one or more simulations; executing the simulation model with data associated with the prioritized nodes in the tree in parallel to determine a posterior probability for each prioritized node; and determining whether each prioritized node is accepted or rejected based on the posterior probabilities. Numerous other aspects are provided.

Massively accelerated Bayesian machine

A testing system computer device for dynamically updating a test plan of an apparatus includes at least one processor in communication with at least one memory device. The testing system computer device is configured to store a plurality of historical data and generate a simulation model of the apparatus based in part on the historical data. The simulation model includes a plurality of inputs and a plurality of outputs of the apparatus. The testing system computer device is also configured to determine a plurality of tests to perform on the apparatus based on the simulation model and the plurality of historical data. The testing system computer device is further configured to receive a plurality of desirability ratings from a user, rank the plurality of tests to perform based on the plurality of desirability ratings, and present the ranked plurality of tests to the user.

Systems and method for dynamic combustion tests

A monitoring system can include a dashboard including a graphical user interface display space. The monitoring system can also include a digital twin validation system including a processor configured to perform operations including rendering, in a graphical user interface display space, an interactive graphical object characterizing a first input value of a first digital model of a plurality of oil and gas industrial machines. The operations can also include receiving data characterizing user interaction with the interactive graphical object. The data characterizing the user interaction can be indicative of the first input value of the first digital model. The operations can further include updating the first digital model to generate updated output values representative of an operational parameter of the plurality of oil and gas industrial machines. The operations can also include rendering, in the graphical user interface display space, a visual representation of the updated output values.

Monitoring multiple industrial machines

A component is provided. The component includes a structure including a plurality of unit cells joined together, each unit cell of the plurality of unit cells having a mass density substantially similar to the mass density of every other unit cell of the plurality of unit cells. The plurality of unit cells includes a first portion of unit cells having a characteristic dimension and a first portion average stiffness, the characteristic dimension of the first portion of unit cells having a first value. The plurality of unit cells also includes a second portion of unit cells having the characteristic dimension and a second portion average stiffness, the characteristic dimension of the second portion of unit cells having a second value different from the first value, wherein the second portion average stiffness differs from the first portion average stiffness.

Components including structures having decoupled structural stiffness and mass density

A controller for use in an additive manufacturing system including a consolidation device configured to consolidate material is provided. The controller is configured to receive a build file for a component including a plurality of build layers, wherein each build layer includes a component outer perimeter, at least one build layer generating function, at least one generating function variable, and at least one generating function constant. The controller is configured to generate at least one control signal to control a power output throughout at least one scan path of the consolidation device across the material for each build layer of the plurality of build layers, the at least one scan path generated based at least partially on the component outer perimeter, the at least one generating function, the at least one generating function variable, and the at least one generating function constant for each build layer.

Systems and methods for advanced additive manufacturing

According to some embodiments, system, apparatus and methods are provided comprising an analytic model for an installed product; an execution platform configured to execute the analytic model; an application programming interface (API) wrapper associated with each of the analytic model and the execution platform, the API wrapper including input information, output information and a technique; and a storage in communication with the analytic model and the execution platform and storing program instructions to perform the functions as follows: transmitting information between the analytic API wrapper and the execution platform API wrapper; and deploying the analytic model to the execution platform based on the transmitted information. Numerous other aspects are provided.

Scalable and secure analytic model integration and deployment platform

A component has a first structural configuration and a second structural configuration. The component includes a sensor assembly including a plurality of interconnected structural members defining a plurality of load paths. A first structural member and a second structural member define a first load path when the component is in the first structural configuration. The first structural member and a third structural member define a second load path when the component is in the second structural configuration. The second load path is configured to bypass the second structural member. The sensor assembly is configured to detect a characteristic of the component that changes when the component switches between the first structural configuration and the second structural configuration.

Growing community of inventors

Clifton Park, NY, United States of America

Arun Karthi Subramaniyan