General Electric Company

Other

Ge Inspection Technologies, Gmbh

Steven Hector Azzaro

Warren F Bessler

Vinay Bhaskar Jammu

Edward Brittain Stokes

Timothy Lee Johnson

Thomas Gary O'Keeffe

Christopher Edward Wolfe

Naresh Sundaram Iyer

Robert Arvin Hedeen

Paul Edward Cuddihy

Daniel Joseph Cleary

Glen Murrell

Guoxiang Liu

Robert Carl Lloyd Klenner

Mahendra L Joshi

Christopher Donald Johnson

Imdad Imam

William David Smith, Ii

Aiman A Abdel-Malek

Robert Douglas Cryer

Vrinda Rajiv

Myron L Smith

Mark Mitchell Kornfein

Thomas George Cook

Mahesh Amritlal Morjaria

Hunt Adams Sutherland

LiJie Yu

Cecil M Daniel

Robert Klenner

Robert John Naumiec

John C Crouse

Virginia Ann Zingelewicz

Slawomir Marian Zaremba

Raymond J Bankert

Jeremiah Francis Donoghue

Larry R Handler

Thomas Shaginaw

Enrique Betancourt

Alfonso M Delgado Cruz

Gregory Dennis Coulter

Panqing Gao

Eric Robertson

James Weston Nash

James Arthur Bush

Thomas Timothy Booth

The current subject matter is related to optimizing resource (e.g., steam) allocation within steam assisted gravity drainage (SAGD) system, and predicting future oil production from production wells of the SAGD system. In some embodiments data characterizing a hypothetical rate of steam injection into injection wells of the SAGD system can be applied within a capacitance resistance model (CRM) to determine an estimated rate of future fluid production, the fluid being a mixture of oil and water. An emulsion viscosity, as well as the estimate rate of future fluid production, can be applied within a Koval fractional flow model to determine an estimate rate of future oil production. In some cases, the hypothetical rate of steam injection can be optimized based on an optimization objective, e.g., a steam-to-oil ratio (SOR).

Rapid steam allocation management and optimization for oil sands

System includes one or more processors that are configured to perform iterations of the following until a predetermined condition is satisfied. The one or more processors are configured to select a modified trial schedule. The modified trial schedule is selected based on initial fluid-extraction data and initial trial schedules and, if available, prior modified trial schedules and prior modified fluid-extraction data from prior iterations. The one or more processors are configured to receive modified fluid-extraction data generated by execution of the modified trial schedule with a designated model of the reservoir. The one or more processors are also configured to update the surrogate model with the modified fluid-extraction data and the modified trial schedule. For at least a plurality of the iterations, the modified trial schedule is selected, at least in part, to reduce uncertainty in a sample space as characterized by the surrogate model.

System and method for generating a schedule to extract a resource fluid from a reservoir

A system includes a schedule generator having one or more processors configured to obtain resource extraction parameters for extracting a resource from a reservoir. The resource extraction parameters include well creation parameters associated with drilling wellbores, well stimulation parameters associated with introducing fracturing fluid into the wellbores, and production parameters associated with extracting the resource through the wellbores. The schedule generator selects initial trial schedules having different values of the resource extraction parameters and receives initial resource output data generated by execution of the initial trial schedules with a designated reservoir model. The schedule generator generates a surrogate model based on the initial resource output data and the initial trial schedules and uses the surrogate model to perform iterations of selecting modified trial schedules until a predetermined condition is satisfied.

System and method for generating a schedule to extract a resource from a reservoir

A system for predicting fall risk for a resident is provided. The system includes a fall risk prediction subsystem, configured to predict a fall risk likelihood for a resident based on data measurements collected from one or more sensors. One or more of the sensors comprise range control radar (RCR) sensors. The fall risk prediction subsystem further includes a fall analysis component configured to analyze the data measurements from one or more of the sensors and a fall risk assessment component configured to predict the fall risk likelihood for the resident based on the analysis.

System and method for predicting fall risk for a resident

Method and systems for diagnosing and treating individuals having a mental health disease that provide health related information about one or more of the individuals wherein at least portion of the information comprises collected data on one or more of the individual's activities; analyzes the health related information according to assess the individual's mental health; and creates a treatment plan based at least in part on the individual's assessed state of mental health.

System and method for in-situ mental health monitoring and therapy administration

The present invention provides a computer implemented method for maintaining a knowledge base. The method taking as input, text examples that are tagged with a structural markup language; and maps knowledge nodes in the knowledge base with the tagged examples to determine: (1) the knowledge nodes that best match the tagged examples, and (2) the knowledge nodes that are best connected to the tagged examples. The results are displayed to the user, who verifies that the tagged examples match the selected knowledge nodes. Based on the user response, indices of the knowledge nodes are updated. The method may further include determining discrepancies between the existing knowledge base and the tagged examples, and further displaying the discrepancies to the user. The user can update the missing information in the knowledge base to remove the discrepancies in the existing knowledge base.

Process for semi-automatic maintenance of a knowledge base using tagged examples

A method of integrating case based reasoning data and FMECA data for product life cycle support. The method comprises creating an access path to case based data using a FMECA classification. The creating includes receiving a case based record including heading data. The heading data includes data elements and the data elements are correlated with data fields in a FMECA database. The FMECA database is searched for an entry that includes one or more of the data elements. The creating further includes determining if the entry should include a reference to the case based record. A new pointer to the case based record is inserted if the determining results in finding that the entry should include a reference to the case based record. The method for integrating case based reasoning data and FMECA data further comprises receiving a request from a user to access the case based data. The case based data is accessed via the FMECA database using the access path in response to the request.

Method, system and computer product for integrating case based reasoning data and failure modes, effects and corrective action data

A system for monitoring the condition of a vehicle has at least one wireless transmitter in on the vehicle, and in communication with the vehicle computer system. Data representative of operating parameters generated during the operation of the vehicle is downloaded to the transmitter, which transmits the data to a wireless receiver positioned proximal to a path of travel of the vehicle. The receiver is linked to a processor for transmission of the data to the processor for storage and processing if necessary. The system may also include at least one sensor, positioned proximal the path of travel, for detecting a physical phenomenon emanating from at least one vehicle component. The sensor generates a signal, which is transmitted to the processor. The processor is capable of analyzing the signal generating data indicative of operating condition of the vehicle component. The sensor, and/or receiver, mounted to a structure positioned proximal the path of travel of the vehicle.

System and method for monitoring the condition of a vehicle

A method for performing failure mode and effects analysis throughout the product life cycle. The method comprises receiving incident data from a requestor. The incident data includes a requestor product and a requestor fault mode. A shared failure mode and effects analysis database is accessed and searched for an existing entry that includes the incident data. The contents of the existing entry are transmitted to the requestor in response to locating an existing entry.

Method, system and computer product for performing failure mode and effects analysis throughout the product life cycle

A status detection apparatus for electrical equipment includes a plurality of distributed multiparameter sensors in a containment vessel or other fluid filled region of the electrical equipment. The sensors are capable of providing data relating to plural parameters of the fluid simultaneously at different positions in the fluid. The data provided by the sensors can be processed to permit localization of incipient faults or can indicate other operating states when combined with known flow data of the fluid through the electrical equipment. A method for detecting status of electrical equipment includes sensing parameters simultaneously at multiple locations in the fluid and determining operating status based on the sensed parameters.

Status detection apparatus and method for fluid-filled electrical equipment

An on-board monitor for a railroad locomotive is disclosed. The on-board monitor interfaces with the controller subsystems of the locomotive to collect parametric performance data. The specific data to be collected and the collection intervals are defined at a remote service center and transmitted to the on-board monitor. The on-board monitor also includes the capability to collect additional data or collect data more frequently in response to the results of certain triggering events.

On-board monitor for railroad locomotive

An intelligent analysis apparatus and method for fluid filled electrical equipment includes sensors for measuring various parameters of the electrical equipment. Analytical models calculate parameters based on measurements of other parameters. The measured and calculated parameters are compared and the result of the comparison is used as an indicator in a causal network. The probabilities of the causal network are recalculated by a belief network. The analytical models are adjusted over time to account for acceptable changes in behavior of the equipment over time. The output of the causal network can be used for diagnostic and prognostic indication.

Intelligent analysis system and method for fluid-filled electrical equipment

A fluid filled transformer including a tank for containing at least primary and secondary windings, a radiator connected to the tank via top and bottom headers, a fluid disposed in the tank, a fluid circulation flow path including passages through the windings, radiator, headers, and at least a portion of the tank, and at least one diagnostic sensor disposed within the fluid circulation flow path for measuring properties of the fluid. By positioning the sensor within the circulation flow path, measured values are more reliable, accurate, and efficiently sensed.

Fluid filled electrical device with diagnostic sensor located in fluid circulation flow path

A locomotive diagnostic system. A first sensor outputs a measurement of a first state variable (such as vibration) of a first locomotive component, such as a blower fan bearing set, and a second sensor outputs a measurement of a second state variable (such as rotational speed) of a second locomotive component, such as a blower fan shaft. The first state variable is indicative of the operation of the first component and is dependent on the second state variable. Data represents, for each of a number of different values of the second state variable, first, second, and third ranges of values of the first state variable which indicate, respectively, normal, worn, and failed operation of the first component. A mechanism, such as a digital computer, determines if the measurement of the first state variable is within the first, second, or third range of values of the first state variable for the measurement of the second state variable.

Locomotive diagnostic system

A locomotive for model-based incipient failure detection includes at least one replaceable unit and at least one sensor to generate signals representative of current engine conditions related to the at least one replaceable unit. A controller includes an embedded replaceable unit model algorithm. Current operating conditions and ambient conditions are utilized within the algorithm to generate a model-based predicted value for the at least one sensor. The controller compares the at least one sensor signals to the model-based predicted values for calculating deviations therebetween and detecting incipient failure of the at least one replaceable unit.

Model based assessment of locomotive engines

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Schenectady, NY, United States of America

Steven Hector Azzaro