General Electric Company

Ge Precision Healthcare LLC

University of Zürich

Sidharth Abrol

Prabhanjana Kalya

David Spencer Ewens

Sandeep Dutta

Bipul Das

Maria Cecilia Mazzaro

Alan Meier Truesdale

Prem Kumar Patchaikani

Sangeeta Balram

Saad Ahmed Sirohey

Awadesh Kumar Tiwari

Karl Dean Minto

Achalesh Kumar Pandey

Matthew John McKeever

Mustafa Tekin Dokucu

Axel Busboom

Rogier Sebastiaan Blom

Corey Nicholas Bufi

Samhitha Palanganda Poonacha

Abhijit Vishwas Patil

Kelvin Rafael Estrada

Sohan Rashmi Ranjan

John Alexander Petzen, Iii

Rajeeva Kumar

Wrichik Basu

Rosa Castane Selga

Charles William Weidner

John Page

Guillaume Becquin

Amy Deubig

Pramod Kumar Yadav

Robert Haley

Vanika Singhal

Ann Sarah

Daphné Gerbaud

Bianca Sintini

Giriprasad Chebiyyam

Ronny Büchel

Philipp Kaufmann

Nisam Palakkavalappil Rahiman

Rasika Suresh Kalwit

Gordan Raymond Smith

John Alexander Petzen

Awadaesh Kumar Tiwari

Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

Learning-based domain transformation for medical images

Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.

Domain adaptation using post-processing model correction

Systems and methods are provided for perioperative care in a medical facility. In an example, a system includes a display and a computing device operably coupled to the display and storing instructions executable to output, to the display, a graphical user interface (GUI) that includes real-time medical device data of a patient, at least some of the real-time medical device data displayed via the GUI as a plurality of patient monitoring parameter tiles, the GUI including a risk score indicative of a relative likelihood that the patient will exhibit a condition within a period of time, and responsive to a user input, display, on the GUI, a set of trend lines each showing values for a respective patient monitoring parameter over a time range, each trend line of the set of trend lines selected based on a contribution of each respective patient monitoring parameter to the risk score.

Systems and methods for graphical user interfaces for medical device trends

This application provides methods and systems for automated condition-based maintenance of mechanical systems. Example systems may at least one memory coupled to one or more computer processors that are configured to receive first data from the mechanical system indicative of performance of a first component of the mechanical system, determine, using the first data, a first performance metric for the first component, determine, using the first performance metric, a probability value that a fault has occurred at the first component, and determine, using the probability value, a predicted length of time until failure of the first component.

Methods and systems for automated condition-based maintenance of mechanical systems

A method for predicting an anomaly in a combustor () is presented. The method includes receiving signals representative of parameters in one or more combustion cans () of the combustor, generating a plurality of patterns based on a permutation entropy window and the signals, identifying a plurality of pattern categories in the plurality of patterns, determining a permutation entropy based on the plurality of patterns and the plurality of pattern categories, and predicting an anomaly in the combustor based on the permutation entropy. The method further includes comparing the plurality of pattern categories to determined permutations of pattern categories if the anomaly is present in the combustor, and predicting a category of the anomaly based on the comparison of the plurality of pattern categories to the determined permutations of pattern categories.

Systems and methods for predicting an anomaly in a combustor

A monitoring system of a combined cycle power plant having a topping cycle performed by a gas turbine and a bottoming cycle performed by a steam turbine, the monitoring system. The monitoring system includes a processor that receives a first set of data indicating a first measurement value of an operating parameter of the combined cycle power plant. The processor determines a baseline efficiency of the process based on the first measurement value using a bottoming cycle performance (BCP) model. The processor determines a threshold based on the baseline efficiency. The processor receives a second set of data indicating a second measurement value of the operating parameter. The processor compare the threshold to an operational efficiency based on the second measurement value using the BCP model, and provides an indication of a decrease in efficiency of the process if the operational efficiency is below the threshold.

Method to diagnose power plant degradation using efficiency models

A non-transitory computer readable medium stores instructions that are executed by a processor of a monitoring that when executed, the processor receives a state of at least one operational parameter of the turbomachinery. Then, the processor determines whether the state is within a region of accuracy of a stored record of a stored state stored in the memory. When the state is within the region of accuracy, the processor determines at least one operating condition from the stored record. Alternatively, when the state is not within the first region of accuracy, the processor stores the state as a new record in the memory.

Efficiency maps for tracking component degradation

A non-transitory computer-readable storage medium storing one or more processor-executable instructions wherein the one or more instructions, when executed by a processor of a controller, cause acts to be performed including receiving signals representative of pressure between respective compressor blade tips and a casing of a compressor at one or more stages, generating multiple patterns based on a permutation entropy window and the signals, identifying multiple pattern categories in the multiple patterns, determining a permutation entropy based on the multiple patterns and the multiple pattern categories, predicting an anomaly in the compressor based on the permutation entropy, comparing the multiple pattern categories to determined permutations of pattern categories when an anomaly is present in the compressor, and predicting a category of the anomaly based on the comparison of the multiple pattern categories to the determined permutation of pattern categories.

Systems and methods for compressor anomaly prediction

A fault detection method for a selective catalytic reduction (SCR) system comprising an SCR reactor, includes receiving a plurality of operating parameters () of the SCR reactor from a plurality of sensors. The method also includes estimating a state of an adaptive reactor model () representative of the SCR reactor based on the plurality of operating parameters. The method also includes generating a feature parameter () based on the plurality of operating parameters and the estimated state of the adaptive reactor model. The method includes determining a fault in the SCR system () based on the feature parameter.

System and method for fault diagnosis in emission control system

A system includes a controller coupled to an inlet bleed heat system of a compressor. The controller is configured to detect an icing condition of an inlet portion of the compressor by determining a current compressor efficiency value of the compressor, a current compressor flow value of the compressor, or any combination thereof, comparing the current compressor efficiency value to a compressor efficiency model of the compressor, the current compressor flow value to a compressor flow model of the compressor, or any combination thereof, and providing an icing indication to the inlet bleed heat system if a first difference between the current compressor efficiency value and the compressor efficiency model is greater than a first threshold, a second difference between the current compressor flow value and the compressor flow model is greater than a second threshold, or any combination thereof.

Systems and methods for icing detection of compressors

A fluid transport system includes at least one flow control device and a multiphase pump configured to transport fluid. At least one pump sensing device is configured to measure at least one operating characteristic of the multiphase pump. A controller is programmed with a pump map including a correlation of the at least one operating characteristic of the multiphase pump with at least one operating characteristic of the fluid. The controller is configured to determine an estimated value of the at least one operating characteristic of the fluid based on the measured value of the at least one operating characteristic of the multiphase pump and the pump map. At least one regulating device coupled to at least one flow control device is modulated based on the estimated value of the at least one operating characteristic of the fluid.

System and method for controlling a fluid transport system

A system includes an emissions control system. The emissions control system includes a processor programmed to receive one or more selective catalytic reduction (SCR) operating conditions for an SCR system. The SCR system is included in an aftertreatment system for an exhaust stream. The processor is also programmed to receive one or more gas turbine operating conditions for a gas turbine engine. The gas turbine engine is configured to direct the exhaust stream into the aftertreatment system. The processor is further programmed to derive a NHflow to the SCR system based on an SCR model and the one or more SCR operating conditions, to derive a NO/NOx ratio, and to derive a fuel split for the gas turbine engine based on the NHflow, the NO/NOx ratio, or a combination thereof.

System and method for emissions control in gas turbine systems

A system may include a memory storing a turbomachinery degradation model configured to model degradation of a turbine system over time. Further, the system may include a controller communicatively coupled to the memory, which derives a turbomachinery wash timing based on at least one input signal from the turbine system and the turbomachinery degradation model. The turbomachinery degradation model may derive a desired wash point by estimating a modeled power of the turbine system, a modeled heat rate of the turbine system, or both. Furthermore, the controller may use the desired wash point to determine a time for washing components of the turbine system.

Wash timing based on turbine operating parameters

A method of controlling a water level in a steam drum includes predicting a transient in the steam drum based on plant characteristics including steam flow from the steam drum, drum pressure in the steam drum, and one or both of a gas turbine load and a position of a bypass valve configured to control the steam flow from the steam drum to two or more steam flow conduits. The method further includes generating a sliding setpoint to control the water level based on predicting the transient in the steam drum.

Controlling boiler drum level

A method of controlling a heat recovery steam generator (HRSG) includes measuring a first regulated output of the HRSG and a second regulated output of the HRSG. The method includes comparing the first regulated output to a first setpoint defining a first target output to generate a first error signal and comparing the second regulated output to a second setpoint defining a second target output to generate a second error signal. The method also includes generating, by a controller implementing a multivariable control algorithm having as inputs the first error signal and the second error signal, control signals to control the HRSG to adjust values of the first regulated output and the second regulated output.

Multivariable controls of heat recovery steam generation system

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Bengaluru, India

Sidharth Abrol