Caterpillar Inc.

James Mason

Anthony James Grichnik

Timothy Joe Felty

Tim Felty

Mary Lou Kesse

Michael Seskin

Amit Jayachandran

Christopher Lewis Wynkoop

Daniela Radakovic

Gavin Peter Williams

Stuart P Smith

Lonny E Johnson

Michael Lee Taylor

Navid Tafaghodi Khajavi

Christos Nikolopoulos

Meredith Jaye Cler

Gabriel Carl Hart

A method for asset identification string error detection can include receiving an asset identification string corresponding to an asset and aggregating data associated with the asset from multiple sources. The method can include creating one or more probabilistic tables linking the aggregated data to the asset and determining a likelihood that at least a portion of the asset identification string is correct based on the probabilistic tables. The method can indicate that the asset identification string includes an error when the likelihood is less than a selected threshold and otherwise indicate that the asset identification string is correct when the likelihood is greater than or equal to the selected threshold.

Systems and methods for asset identification string error detection and correction

A controller implemented method of remotely operating a machine includes determining, for each of a plurality of operating conditions of the machine, a predetermined configuration of a machine instrument array and removably mounting a portable computing device on a remote control console. The method further includes defining a configuration of a console instrument array of the remote control console in which, for each of the plurality of operating conditions, the configuration of the console instrument array is generally identically to the predetermined configuration of the machine instrument array. A system is also disclosed.

Remote control system for a machine

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 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 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 method for providing a virtual sensor network based system. The method may include obtaining project data descriptive of a virtual sensor network to be used in a control system of a machine; establishing a virtual sensor network including a plurality of virtual sensors based on the project data. Each virtual sensor may have a model type, at least one input parameter, and at least one output parameter. The method may also include recording model information, measurement data, and performance information of the virtual sensor network including the plurality of virtual sensors; creating one or more calibration certificates of the virtual sensor network including a plurality of virtual sensors based on the model information, the measurement data, and the performance information; and generating a documentation package associated with the virtual sensor network. The documentation package may include at least an identification, the project data, and at least one calibration certificate of the virtual sensor network including a plurality of virtual sensors.

Virtual sensor network (VSN) based control system and method

System and methods are provided for applying a dimensionality reduction process to a data set. The method includes obtaining a data set including a first set of variables and identifying collections of the first set of variables for replacement by a second set of variables. The method also includes replacing the collections of the first set of variables with the second set of variables and eliminating the first set of variables from the data set.

Method and system for reducing a data set

One aspect of the present disclosure includes a method for a control system of a machine. The method may include establishing a virtual sensor model indicative of interrelationships between at least one sensing parameter and a plurality of measured parameters related to the machine. The method may also include obtaining data and function information representing the virtual sensor model and converting the data information into fixed-point representation. Further, the method may include converting the function information into fixed-point representation and loading the converted fixed-point representation of data information and function information in the control system such that the control system uses the virtual sensor model in fixed-point arithmetic operation.

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Peoria, IL, United States of America

James Mason