Abstract: A computer-implemented method for simulating movement of materials within a facility using predefined software objects is disclosed. The method includes storing a plurality of predefined software objects in a computer memory, each of the predefined software objects including one or more discrete event simulator commands and representing a process performed at a facility. The method also includes receiving, by one or more processors, an instruction to simulate movement of a material within a facility, and linking, by the one or more processors, two or more of the predefined software objects together to represent the movement of the material within the facility. The method further includes executing, by the one or more processors, the linked predefined software objects using a discrete event simulator to simulate the movement of the material within the facility.

Abstract: A computer-implemented method for simulating a facility to achieve one or more business goals is disclosed. The method includes receiving, by a processor, one or more input parameters for a discrete event simulator (DES) module for a facility, a plurality of business goals for the facility, and a selection of a goal optimization technique. The method also includes generating, by the processor, a DES output that simulates processes occurring within the facility based on the one or more input parameters and simultaneously achieves the business goals in accordance with the selected goal optimization technique.

Abstract: A sensor error detection and compensation system is disclosed. The system may have a sensor state estimation module that is configured to generate a physical sensor confidence value representing an accuracy estimation of a physical sensor output value received from a physical sensor. The system may also have a sensor output aggregation module configured to determine an aggregated sensor value based on the physical sensor confidence value, the physical sensor output value, a virtual sensor output value received from a virtual sensor, and a virtual sensor confidence value representing an accuracy estimation of the virtual sensor output value. Moreover, the system may have a replace sensor decision module configured to determine whether the physical sensor has failed by comparing the physical sensor confidence value to a replacement threshold level.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 determine a forecast function that includes one or more variables and represents forecasted characteristics of the target item. The processor may also be enabled to implement a genetic algorithm to generate one or more chromosomes having a data value for each of the variables of the forecast function, determine a chromosome value for at least one of the chromosomes that is based on a goal function including one or more measurable business goals. Moreover, the processor may be further enabled to select a chromosome from among the one or more chromosomes based on the chromosome value, and forecast the characteristics of the target item using the selected chromosome.

Abstract: 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 collect historical data associated with characteristics of a target item, and modulate the historical data with a modulator signal. The processor may also be enabled to determine an intermediary function that includes one or more variables, and implement a genetic algorithm to determine a data value for each of the variables of the intermediary function. Moreover, the processor may be enabled to solve the intermediary function using the data values determined by the genetic algorithm, and generate a forecast function representing forecasted characteristics of the target item by subtracting the modulator signal from the intermediary function.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer system for determining a combined risk is disclosed. The computer system has a memory, at least one input device, and a central processing unit in communication with the memory and the at least one input device. The central processing unit obtains diagnostic data and identifies a plurality of models for analyzing the diagnostic data. The central processing unit also associates each model with one of a plurality of time periods and calculates, for each time period using the associated model, a predicted risk. Further, the central processing unit determines the combined risk based on the predicted risk for each time period.

Abstract: A method for simplifying a mathematical model is disclosed. The method obtains a data set and identifies a plurality of variables within the data set. The method also performs a clustering analysis by dividing the data set into groups, where each group has a cluster center. The method further replaces the plurality of variables with a plurality of cluster distances. The method also uses the plurality of cluster distances as a plurality of independent variables in a model creation process.

Abstract: A method for analyzing machine customization costs includes receiving one or more specifications associated with a machine and identifying a machine type based on the one or more specifications. Prognostic data associated with the machine type is analyzed based on the specifications, and costs associated with operating a stock machine corresponding to the machine type is estimated based on the prognostic data analysis. A machine customization package may be assembled based on the specifications and costs associated with operating a customized machine associated with the machine customization package may be analyzed. A cost analysis report that compares estimated costs associated with operating the stock machine with estimated costs associated with operating the customized machine is provided.

Abstract: A method for determining risk associated with delaying machine maintenance includes receiving health data associated with a machine and analyzing the health data based on predetermined threshold data to detect an event alert condition. A future failure date of a machine component is then estimated based on the health data analysis. Historic productivity data associated with the machine is analyzed and a cost associated with operating the machine with the event alert condition for a predetermined amount of time is estimated. A cost associated with removing the machine from service is predicted based on historic productivity analysis. The estimated cost associated with removing the machine from service is then analyzed based on the estimated cost associated with operating the machine with the event alert condition. A machine repair recommendation indicative of the cost analysis is provided.

Abstract: A performance-based job site management system for a machine environment includes a plurality of monitoring devices for collecting productivity data associated with a machine and transmitting the productivity data to an on-board data collector associated with the machine. The system also includes a project management system communicatively coupled to the on-board data collector. The project management system is configured to receive the productivity data from the on-board data collector and analyze the productivity data based on predetermined performance criteria. The system also assesses machine performance based on the productivity analysis and evaluate job site performance based on the machine performance. The system also provides recommendations for improving job site performance based on the evaluation and adjusts one or more operational aspects associated with the machine, based on the recommendations.

Abstract: A method for selective, event-based communication of machine data includes receiving, in an on-board controller, operation data associated with a component of a machine and comparing the operation data with predetermined threshold data associated with the component. An event alert is generated if the operation data is inconsistent with the predetermined threshold data. The method also includes collecting, in an operational monitoring system associated with the machine, the operation data associated with the component of the machine in response to the event alert. A diagnostic analysis of the operation data associated with the component is performed, the results of which may be to a user of the operational monitoring system.

Abstract: A method for selective on-board processing of machine data includes receiving operation data associated with a machine and collecting the received operation data in an on-board processing unit of the machine. Required data associated with a data analysis task of the on-board processing unit may be identified and the received operation data may be analyzed based on the identified required data. The method may also include determining whether required data associated with the data analysis task is accessible by the on-board processing unit, based on the analysis. If any portion of the required data is inaccessible by the on-board processing unit, the inaccessible portion of the required data may be requested from an external system.