Abstract: Systems and methods are disclosed for forecasting future sales of a prime product. The system includes at least one processor configured with instructions to collect historical sales data of the prime product, collect historical telematics data from one or more machines that were sold as the prime product, and collect historical econometric data relevant to the prime product. The at least one processor also generates a group of candidate predictors from the historical telematics data and the historical econometric data, select predictors from the group of candidate predictors, and establish a forecasting model representing a relationship between the selected predictors and the historical sales data of the prime product. The at least one processor forecasts future sales of the prime product by using the established forecasting model.

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: Diaphragm assemblies for use with fluid control devices are described. An example diaphragm assembly includes a body defining a plurality of chambers to be fluidly coupled to an outlet pressure of a fluid control device. Each of the chambers is spaced along a stem at least partially disposed in the chambers, and the stem is to move to control a flow of fluid through the fluid control device. Additionally, the example diaphragm assembly includes a first plurality of diaphragms, each of which is disposed in a corresponding one of the chambers, and each of which is coupled to the stem to move the stem in response to the outlet pressure of the fluid control device.

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 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: 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.

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: Diaphragm assemblies for use with fluid control devices are described. An example diaphragm assembly includes a body defining a plurality of chambers to be fluidly coupled to an outlet pressure of a fluid control device. Each of the chambers is spaced along a stem at least partially disposed in the chambers, and the stem is to move to control a flow of fluid through the fluid control device. Additionally, the example diaphragm assembly includes a first plurality of diaphragms, each of which is disposed in a corresponding one of the chambers, and each of which is coupled to the stem to move the stem in response to the outlet pressure of the fluid control device.

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: 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.

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.