Caterpillar Inc.

Solar Turbines Inc.

Caterpiller Inc.

Anthony James Grichnik

Michael Seskin

James Mason

Amit Jayachandran

Timothy Joe Felty

Mary Lou Kesse

Tim Felty

Christopher Lewis Wynkoop

Michael Lee Taylor

Wade S Willden

Evan Earl Jacobson

Stephan Donald Roozenboom

David M Milam

Roy Thomas Collins

Gavin Peter Williams

Burton Roland Clarke

Stuart P Smith

Kevin C Brady

John M Holm

Christos Nikolopoulos

Susan Grimes

Meredith Jaye Cler

Gabriel Carl Hart

Tatiana Kalganova

Andrea Brazzale-Anderson

Amar Patel

Marco Perez

Kermit H Dove, Jr

Danny L Weis

Stephen James Forret

Brian Lee Boggs

Scott Edward Taylor

A control system is disclosed. The control system may have a physical sensor that may output a physical output value, and a virtual sensor network that may output a virtual output value. The control system may also have an electronic control module that may be configured to receive the physical output value from the physical sensor and receive the virtual output value from the virtual sensor. The electronic control module may be further configured to determine a trust level of the physical sensor based on the physical output value, and determine that the trust level of the physical sensor is below a first threshold trust value. The electronic control module may control a machine based on the virtual output value responsive to determining that the trust level of the physical sensor is below the first threshold trust value.

Systems and methods for extending physical sensor range using virtual sensors

A characteristic forecasting system is disclosed. The characteristic forecasting system may have a memory and a processor. The memory may store instructions, that, when executed, enable the processor to generate at least one chromosome using a genetic algorithm, the chromosome including data values for variables of one or more equations used to generate forecast data for a target item. The processor may also be enabled to calculate a chromosome value for the chromosome based on a goal function associated with the genetic algorithm and determine at least one process parameter value for the chromosome at a time interval of the forecast data. The processor may also compare the process parameter value to a process constraint value representing a process limitation associated with the target item and modify the chromosome value for the chromosome responsive to a determination that the process parameter value does not satisfy the process constraint value.

Systems and methods for forecasting using process constraints

A control system is disclosed. The control system may have a physical sensor configured to measure physical parameter values of a machine. The control system may also have a virtual sensor network system configured to receive the physical parameter values measured by the physical sensor as input parameter values, and generate output parameter values based on the input parameter values. Further, the control system may have an electronic control module configured to store an output parameter value default rate of change and an output parameter value threshold rate of change, compare a rate of change of the output parameter values generated by the virtual sensor network system to the output parameter value threshold rate of change, and control the machine based on the output parameter value default rate of change if the rate of change of the output parameter values exceeds the output parameter value threshold rate of change.

Virtual sensor system and method for generating output parameters

A characteristic forecasting system is disclosed. The characteristic forecasting system may have a memory module and a processor. The memory module may store instructions, that, when executed, enable the processor to generate forecast data for a target item based on historical data related to the target item, the forecast data representing a forecast for characteristics of the target item. The processor may also be enabled to analyze a subset of the historical data within an observation window to determine whether the forecast data should be updated with new forecast data and generate the new forecast data for the target item responsive to a determination that the forecast data should be updated with the new forecast data.

Systems and methods for selectively updating forecast data

A characteristic forecasting system is disclosed. The characteristic forecasting system may have a memory module and a processor. The memory module may store instructions, that, when executed, enable the processor to generate a forecast function that represents forecasted characteristics of a target item and includes one or more continuous functions and one or more attenuating functions. Each of the attenuating functions may attenuate forecast values generated by the continuous functions at periodic time intervals. The processor may also be enabled to forecast the characteristics of the target item using the forecast function.

Systems and methods for forecasting using an attenuated forecast function

A method is provided for a virtual sensor system. The method may include obtaining data records associated with a plurality of input parameters and at least one output parameter and adjusting values of the input parameters based on autocorrelation of respective input parameters. The method may also include reconfiguring the input parameters based on cross-correlation of respective input parameters relative to the output parameter and establishing a first virtual sensor process model indicative of interrelationships between the adjusted and reconfigured input parameters and the output parameter.

Virtual sensor based engine control system and method

A method is provided for model optimization. The method may include obtaining respective distribution descriptions of a plurality of input parameters to a model indicative of interrelationships between the input parameters and one or more output parameters. The method may also include specifying respective search ranges for the plurality of input parameters and simulating the model to determine a desired set of input parameters based on a zeta statistic of the model. Further, the method may include determining respective desired distributions of the input parameters based on the desired set of input parameters; determining significance levels of the input parameters in interacting with the output parameter based on the simulation and the desired distributions of the input parameters; and presenting the significance levels.

Process modeling and optimization method and system

System and methods are provided for prioritizing communication of a health risk. The method may include obtaining health information for individuals and determining, based on the health information, a risk of each individual contracting each of plurality of diseases. The method may also include calculating, based on the risks, an average risk of each individual contracting the diseases and selecting a subset of the individuals based on the average risks. Further, the method may include calculating an improved risk by making lifestyle changes for the subset of the individuals and communicating the health risks and the lifestyle changes to individuals in the subset based on the amount of improved risk.

Method and system for prioritizing communication of a health risk

A method is used for providing sensing data to a control system of a machine. The method may include providing a plurality of virtual sensors, each of which may have a model type, at least one input parameter, and at least one output parameter. The method may also include integrating the plurality of virtual sensors into a virtual sensor network; determining interdependencies among the plurality of virtual sensors; and obtaining operational information of the plurality of virtual sensors. Further, the method may include recording measurement data and performance information of the virtual sensor network and the plurality of virtual sensors; and generating one or more calibration certificate of the virtual sensor network based on the operational information, the measurement data, and the performance information.

Calibration certificate for virtual sensor network (VSN)

A method is provided for designing a product. The method may include obtaining data records relating to one or more input variables and one or more output parameters associated with the product and selecting one or more input parameters from the one or more input variables. The method may also include generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records and providing a set of constraints to the computational model representative of a compliance state for the product. Further the method may include using the computational model and the provided set of constraints to generate statistical distributions for the one or more output parameters. The one or more input parameters and the one or more output parameters represent a design for the product.

Asymmetric random scatter process for probabilistic modeling system for product design

A method for transforming machine sensor data between a time domain and a combustion pulse domain is disclosed. The method may include determining a number of pulse events per rotation of a shaft and determining a sampling rate per pulse event. The method may also include sampling one or more sensors at the sampling rate per pulse event. The method also may include transforming the samples into the combustion pulse domain.

System and method for transforming data between the time domain and the combustion pulse domain

A method for modifying data coverage in a modeling system is disclosed. The method may include obtaining data records relating to a plurality of input variables and one or more output parameters and selecting a plurality of input parameters from the plurality of input variables. The method may further include evaluating a coverage of the data records in a modeling space and modifying the coverage of the data records, if a data coverage condition is detected. The method may also include generating a computational model indicative of interrelationships between the plurality of input parameters and the one or more output parameters based on the data records.

System and method for improving data coverage in modeling systems

A computer-implemented method for pre-processing data. The method may include detecting one or more erroneous vectors in a plurality of vectors, detecting one or more erroneous elements in the one or more erroneous vectors, and deleting the detected one or more erroneous elements. The method may also include detecting one or more missing elements in the plurality of vectors. Further, the method may include populating one or more offending vectors that include one or more missing elements and/or deleted erroneous elements with one or more elements that are based on a distance metric.

Method and system for pre-processing data using the mahalanobis distance (MD)

A method is used for providing sensing data to a control system of a machine. The method may include providing a plurality of virtual sensors, each of which may have a model type, at least one input parameter, and at least one output parameter. The method may also include integrating the plurality of virtual sensors into a virtual sensor network; determining interdependencies among the plurality of virtual sensors; and obtaining operational information of the plurality of virtual sensors. Further, the method may include determining a first condition under which the virtual sensor network is unfit to provide one or more virtual sensor output parameter to the control system based on the determined interdependencies and the operational information; and presenting the determined first condition to the control system.

Virtual sensor network (VSN) system and method

A computer-implemented method is provided for determining a corrective action. The method may include obtaining diagnostic data and calculating, using the diagnostic data, a prognostic. The method may also include retrieving, when the prognostic is above an alarm level, historical records and calculating a Bayesian posterior probability using the diagnostic data and the historical records. Further, the method may include calculating a prognostic prescience metric using the diagnostic data and the historical records and determining, based on the Bayesian posterior probability, the prognostic prescience metric, and an economic calculation, the corrective action.

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Peoria, IL, United States of America

Anthony James Grichnik