General Electric Company

Saudi Arabian Oil Company

Avitas Systems, Inc.

Exxonmobil Upstream Research Company

Utc Fire & Security Americas Corporation, Inc.

Ser Nam Lim

David Scott Diwinsky

Xiao Bian

Li Guan

Arpit Jain

Ayman Amer

Vincent Cunningham

Ting Yu

Ahmad Aldabbagh

Mustafa Gokhan Uzunbas

Mustafa Uzunbas

Muhammad Ababtain

Peter Henry Tu

Jens Rittscher

Xiaoming Liu

Ali Al Shehri

Russell Robert Irving

Joseph Vincent Pawlowski

Shubao Liu

Yen-Liang Lin

Xingwei Yang

Yogesh Agarwal

Srivatsa Dhanvantri

Mustafa Devrim Kaba

Sravanthi Bondugula

Binod Pandey

Swaminathan Sankaranarayanan

Wayne Ray Grady

Ya Xue

Jilin Tu

Jie Yu

Ali Shehri

Kedar Anil Patwardhan

John Karigiannis

John Brandon Laflen

Nils Oliver Krahnstoever

Ashish Jain

John Robert Hare

Wei Wang

Ning Zhou

Stephane Harel

Vladimir Shapiro

Steeves Bouchard

Vinod Padmanabhan Kumar

David Cantin

Jose Abiel Garza

Jiajia Luo

Nicolas Saudrais

Jean-Philippe Choiniere

Maxime Beaudoin Pouliot

Weiwei Qian

Jingjing Zheng

Joao Vitor Baldini Soares

Craig Ronald Hoffmann

John Boot

Godine Kok Yan Chan

Mohamad Bagheri Esfe

Kari Thompson

An apparatus, system and method of providing industrial analytics. The apparatus, system and method include at least a plurality of sensors sensing performance indicators for an industrial process across multiple nodes, wherein ones of the multiple nodes are remote from each other; at least one machine learning module comprising non-transitory computing code executed by a processor. When executed by the processor, the code causes the steps of: receiving user input regarding at least the industrial process and a data set; selecting a model based on at least the user input, wherein the selected model comprises a plurality of learnings based on the performance indicators sensed by multiple sensors across at least multiple ones of the multiple nodes; applying the selected model to the data set; assessing at least the performance indicators for the data set upon application of the selected model; and outputting the assessed performance indicator.

Apparatus, system and method for developing industrial process solutions using artificial intelligence

An optical imaging and processing system includes an optical element and a processor configured to process the plurality of image frames to generate a three-dimensional model of at least a portion of the turbine component interior. The system may also include a display coupled to the processor to display the three-dimensional model. An operator may view and analyze the three-dimensional model on the display for defects. The processor may further be configured to automatically navigate the three-dimensional model to determine defects within the turbine component interior. The system may also include a repositioning device configured to reposition the optical element such that the optical element may capture the plurality of image frames from multiple vantage points within the turbine component interior.

System and method for engine inspection

A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.

Cloud-based machine learning system and data fusion for the prediction and detection of corrosion under insulation

An optical imaging and processing system includes an optical connection and an optical element disposed at a first end of the optical connection. The first end of the optical connection is configured to extend into a turbine component interior such that the optical element is disposed within the turbine component interior. The system also includes a photodiode array disposed at a second end of the optical connection, where the optical element is configured to transmit a video stream of the turbine component interior to the photodiode array as the optical element is moved within the turbine component interior between multiple vantage points. The system also includes a processor coupled to the photodiode array, wherein the processor is configured to process the video stream to generate a three-dimensional model of at least a portion of the turbine component interior without utilizing a previously defined three-dimensional model input of the turbine component interior.

A system includes one or more processors configured to automatically identify different distressed portions in repeating segments of a rotating body. At least one of a size and/or a shape of one or more of the distressed portions changes with respect to time. The one or more processors also are configured to determine a pattern of the different distressed portions in the repeating segments of the rotating body during rotation of the rotating body based on identifying the different distressed portions. The one or more processors also are configured to subsequently automatically identify locations of individual segments of the repeating segments in the rotating body using the pattern of the distressed portions that is determined.

Machine learning system for in-situ recognition of common locations in a rotatable body with repeating segments

Machine learning system and data fusion for optimization of deployment conditions for detection of corrosion under insulation

Systems and methods are provided relating to artificial neural networks are provided. The systems and methods obtain a teacher network that includes artificial neural layers configured to automatically identify one or more objects in an image examined by the artificial neural layers, receive a set of task images at the teacher network, examine the set of task images with the teacher network, identify a subset of the artificial neural layers that are utilized during examination of the set of task images with the teacher network, and define a student network based on the set of task images. The student network is configured to automatically identify one or more objects in an image examined by the subset.

Neural network systems

A method includes determining object class probabilities of pixels in a first input image by examining the first input image in a forward propagation direction through layers of artificial neurons of an artificial neural network. The object class probabilities indicate likelihoods that the pixels represent different types of objects in the first input image. The method also includes selecting, for each of two or more of the pixels, an object class represented by the pixel by comparing the object class probabilities of the pixels with each other, determining an error associated with the object class that is selected for each pixel of the two or more pixels, determining one or more image perturbations by back-propagating the errors associated with the object classes selected for the pixels of the first input image through the layers of the neural network without modifying the neural network, and modifying a second input image by applying the one or more image perturbations to one or more of the first input image or the second input image prior to providing the second input image to the neural network for examination by the neurons in the neural network for automated object recognition in the second input image.

Image analysis neural network systems

A system includes one or more processors configured to analyze obtained image data representing a rotor blade to detect a candidate feature on the rotor blade and determine changes in the size or position of the candidate feature over time. The one or more processors are configured to identify the candidate feature on the rotor blade as a defect feature responsive to the changes in the candidate feature being the same or similar to a predicted progression of the defect feature over time. The predicted progression of the defect feature is determined according to an action-guidance function generated by an artificial neural network via a machine learning algorithm. Responsive to identifying the candidate feature on the rotor blade as the defect feature, the one or more processors are configured to automatically schedule maintenance for the rotor blade, alert an operator, or stop movement of the rotor blade.

Neural network feature recognition system

A generative adversarial network (GAN) system includes a generator sub-network configured to examine images of an object moving relative to a viewer of the object. The generator sub-network also is configured to generate one or more distribution-based images based on the images that were examined. The system also includes a discriminator sub-network configured to examine the one or more distribution-based images to determine whether the one or more distribution-based images accurately represent the object. A predicted optical flow of the object is represented by relative movement of the object as shown in the one or more distribution-based images.

Optical flow determination system

A generative adversarial network (GAN) system includes a generator neural sub-network configured to receive one or more images depicting one or more objects. The generator neural sub-network also is configured to generate a foreground image and a background image based on the one or more images that are received, the generator neural sub-network configured to combine the foreground image with the background image to form a consolidated image. The GAN system also includes a discriminator neural sub-network configured to examine the consolidated image and determine whether the consolidated image depicts at least one of the objects. The generator neural sub-network is configured to one or more of provide the consolidated image or generate an additional image as a training image used to train another neural network to automatically identify the one or more objects in one or more other images.

Visual anomaly detection system

A method includes obtaining a series of images of a rotating target object through multiple revolutions of the target object. The method includes grouping the images into multiple, different sets of images. The images in each of the different sets depict a common portion of the target object. At least some of the images in each set are obtained during a different revolution of the target object. The method further includes examining the images in at least a first set of the multiple sets of images using an artificial neural network for automated object-of-interest recognition by the artificial neural network.

A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.

Equipment damage prediction system using neural networks

The present disclosure is directed to a computer-implemented method of sensor planning for acquiring samples via an apparatus including one or more sensors. The computer-implemented method includes defining, by one or more computing devices, an area of interest; identifying, by the one or more computing devices, one or more sensing parameters for the one or more sensors; determining, by the one or more computing devices, a sampling combination for acquiring a plurality of samples by the one or more sensors based at least in part on the one or more sensing parameters; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination.

Robotic sensing apparatus and methods of sensor planning

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Niskayuna, NY, United States of America

Ser Nam Lim