An industrial data presentation system leverages structured data types defined on industrial devices to generate and deliver meaningful presentations of industrial data. Industrial devices are configured to support structured data types referred to as basic information data types (BIDTs) comprising a finite set of structured information data types, including a rate data type, a state data type, an odometer data type, and an event data type. The BIDTs can be referenced by both automation models of an industrial asset and non-automation models of the asset, allowing data points of both types of models to be easily linked using a common data source nomenclature.

Catalysts used for catalytic reforming are treated with organic chloride to condition the catalysts. Chloride treaters may be located in the product streams to remove the chloride contaminants. The continuous catalyst reforming process, including the catalyst reformer unit and chloride treaters, may be monitored in order to predict when adsorbent replacement or regeneration is needed. For example, one or more sensors and measurement devices may be used to monitor certain conditions or parameters. A system may be configured to take one or more actions in response to certain conditions or parameters being met.

A compressor controller for operating a compressor within an industrial automation environment is provided. The compressor controller includes a control module, configured to control the compressor via control settings, and a machine learning module, coupled with the control module. The machine learning module is configured to receive a set of supervised data related to the compressor, and to train with the supervised data to produce a Newtonian physics model representing the inputs and outputs of the compressor within the industrial automation environment. The machine learning module is also configured to receive performance data related to the compressor, receive environment data related to the compressor, and to process the performance data and environment data to produce predicted future performance data for the compressor, and to produce control settings for the compressor.

A method and a corresponding system and computer program are provided. A model of an object to be manufactured via subtractive manufacturing is obtained. Geometric features to be machined as part of manufacturing the object are identified based on the model. The identified geometric features include a composite geometric feature including a plurality of geometric subfeatures. A database including strategies for machining different geometric features is accessed. The database includes a composite strategy for machining the composite geometric feature and separate strategies for machining the respective geometric subfeatures. Strategies for machining the respective geometric features are selected from the strategies included in the database. Instructions for causing one or more machine tools to manufacture the object in accordance with the selected strategies are provided. Selecting strategies for machining the respective geometric features via subtractive manufacturing includes selecting the composite strategy for machining the composite geometric feature.

Techniques are described for receiving manufacturing data and events over real time and non-real time interfaces and associating the data with one another. In one example, real time data is received, the real time data associated with a counter value assigned by a precision counter. The received real time data is stored in a storage buffer, and non-real time data is received, where the non-real time data associated with a counter value assigned by the precision counter. In response to receiving the non-real time data, the buffer is searched for real time data having a matching counter value and, in response to identifying stored real time data associated with a matching counter value, the non-real time data is associated with the real time data based on the match. Data packages are generated including related real time and non-real time data associated with matching counter values.

A control device is connected to a servo mechanism that drives a controlled object and outputs a manipulated variable to the servo mechanism so that a controlled variable tracks a target trajectory. The control device includes a controller and a sensor. The controller acquires a measured value from the sensor and performs model predictive control for each control period using a dynamics model representing a relationship between the manipulated variable and the position of the controlled object to generate the manipulated variable to be output to the servo mechanism. The sensor measures the position of the controlled object. The controller performs model predictive control in a first mode using the measured value when the controlled object has a position within the range, and performs model predictive control in a second mode using an output value of the dynamics model when the controlled object has a position outside the range.

An expandable valved conduit for pediatric right ventricular outflow tract (RVOT) reconstruction is disclosed. The valved conduit may provide long-term patency and resistance to thrombosis and stenosis. The valved conduit may enlarge radially and/or longitudinally to accommodate the growing anatomy of the patient. Further, a method is disclosed for the manufacture of the valved conduit based in part on a plastically deformable biocompatible polymer and a computer-optimized valve design developed for such an expandable valved conduit.

A method of monitoring a machine is described. The machine includes a mechanical system moved by a motor, where the mechanical system has more than two components coupled to each other. The two or more components move differently when the mechanical system is driven by the motor. The method includes repeatedly determining one movement factor of one of the components, and repeatedly determining one dynamic factor of one of the components. The movement factors of the remaining components are then calculated via a model of the mechanical system, and separate parameters for the components of the mechanical system are determined from the movement factor, the dynamic factor, and the calculated movement factors.

An operation command generation device includes movement curve designation circuitry configured to determine a movement curve describing an operation of each of at least one mechanism element included in a virtual mechanism, and operation command generation circuitry configured to generate an operation command to control an actual mechanism based on the movement curve.

A control device is connected to a servo mechanism that drives a controlled object and outputs a manipulated variable to the servo mechanism so that a controlled variable tracks a target trajectory. The control device includes a controller and a sensor. The controller acquires a measured value from the sensor and performs model predictive control for each control period using a dynamics model representing a relationship between the manipulated variable and the position of the controlled object to generate the manipulated variable to be output to the servo mechanism. The sensor measures the position of the controlled object. The controller performs model predictive control in a first mode using the measured value when the controlled object has a position within the range, and performs model predictive control in a second mode using an output value of the dynamics model when the controlled object has a position outside the range.