A rotating equipment system with in-line drive-sense circuit (DSC) electric power signal processing includes rotating equipment, in-line drive-sense circuits (DSCs), and one or more processing modules. The in-line DSCs receive input electrical power signals and generate motor drive signals for the rotating equipment. An in-line DSC receives an input electrical power signal, processes it to generate and output a motor drive signal to the rotating equipment via a single line and simultaneously senses the motor drive signal via the single line. Based on the sensing of the motor drive signal via the single line, the in-line DSC provides a digital signal to the one or more processing modules that receive and process the digital signal to determine information regarding one or more operational conditions of the rotating equipment, and based thereon, selectively facilitate one or more adaptation operations on the motor drive signal via the in-line DSC.

A rotating equipment system with in-line drive-sense circuit (DSC) electric power signal processing includes rotating equipment, in-line drive-sense circuits (DSCs), and one or more processing modules. The in-line DSCs receive input electrical power signals and generate motor drive signals for the rotating equipment. An in-line DSC receives an input electrical power signal, processes it to generate and output a motor drive signal to the rotating equipment via a single line and simultaneously senses the motor drive signal via the single line. Based on the sensing of the motor drive signal via the single line, the in-line DSC provides a digital signal to the one or more processing modules that receive and process the digital signal to determine information regarding one or more operational conditions of the rotating equipment, and based thereon, selectively facilitate one or more adaptation operations on the motor drive signal via the in-line DSC.

A feedback control device that takes information regarding a control deviation between a measured value and a desired value of a controlled object as input, and outputs a manipulated variable for the controlled object, includes: a first control unit that takes information regarding the control deviation as input, and outputs a manipulated variable for the controlled object; a second control unit that takes information regarding the control deviation as input, and that includes a learning control unit in which a parameter for outputting a manipulated variable for the controlled object is determined by machine learning; and an adder that adds a first manipulated variable output from the first control unit and a second manipulated variable output from the second control unit. A manipulated variable from the adder is output to the controlled object, and the second control unit includes a limiter that limits the second manipulated variable.

Systems and methods include a method for multi-segmented oil production. A multi-segmented well production model representing production at a multi-segmented oil production facility is calibrated. The model models production based on well rates and flowing bottom-hole pressure data at various choke settings for multiple flow conditions for each segment of the multi-segmented well. Real-time updates to the well rates and the flowing bottom-hole pressure data are received. Changes to triggers identifying thresholds for identifying production improvements are received. The model is re-calibrated based on the changes to the triggers and the real-time updates. An optimization algorithm is executed to determine new optimal inflow control valve (ICV) settings. Using the re-calibrated multi-segmented well production model, a determination is made whether the new optimal ICV settings improve production. If so, the optimal ICV settings are provided to a control panel for the multi-segmented oil production facility.

Systems and methods include a method for multi-segmented oil production. A multi-segmented well production model representing production at a multi-segmented oil production facility is calibrated. The model models production based on well rates and flowing bottom-hole pressure data at various choke settings for multiple flow conditions for each segment of the multi-segmented well. Real-time updates to the well rates and the flowing bottom-hole pressure data are received. Changes to triggers identifying thresholds for identifying production improvements are received. The model is re-calibrated based on the changes to the triggers and the real-time updates. An optimization algorithm is executed to determine new optimal inflow control valve (ICV) settings. Using the re-calibrated multi-segmented well production model, a determination is made whether the new optimal ICV settings improve production. If so, the optimal ICV settings are provided to a control panel for the multi-segmented oil production facility.

The present disclosure relates to reducing software development man-hours when vehicle models are developed. In an in-vehicle equipment controller, a control section has, as a software configuration: an application layer in which an application for in-vehicle equipment is implemented; a device driver; and a middleware layer in which a communication path is generated between the application and the device driver. The middleware layer includes a physical quantity conversion module that converts physical quantity data according to a specified conversion rule, the physical quantity data being included in data transferred through the communication path.

The present disclosure relates to reducing software development man-hours when vehicle models are developed. In an in-vehicle equipment controller, a control section has, as a software configuration: an application layer in which an application for in-vehicle equipment is implemented; a device driver; and a middleware layer in which a communication path is generated between the application and the device driver. The middleware layer includes a physical quantity conversion module that converts physical quantity data according to a specified conversion rule, the physical quantity data being included in data transferred through the communication path.

A priori geo-referenced data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the geo-referenced data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

A rotating equipment system with in-line drive-sense circuit (DSC) electric power signal processing includes rotating equipment, in-line drive-sense circuits (DSCs), and one or more processing modules. The in-line DSCs receive input electrical power signals and generate motor drive signals for the rotating equipment. An in-line DSC receives an input electrical power signal, processes it to generate and output a motor drive signal to the rotating equipment via a single line and simultaneously senses the motor drive signal via the single line. Based on the sensing of the motor drive signal via the single line, the in-line DSC provides a digital signal to the one or more processing modules that receive and process the digital signal to determine information regarding one or more operational conditions of the rotating equipment, and based thereon, selectively facilitate one or more adaptation operations on the motor drive signal via the in-line DSC.

A method for managing smart alarms in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured or derived by at least one intelligent electronic device of a monitoring and control system to identify at least one of power events in the electrical system, or alarms triggered in response to any identified power events. Information related to the identified power events and the identified alarms may be aggregated, and relevant event and/or alarm management groups and/or relevant event and/or alarm periods may be identified from the aggregated information. One or more actions may be triggered, avoided or avoid triggering in response to the identified event management groups and/or the identified event and/or alarm periods. Systems for managing smart alarms are also provided herein.