A smart gateway platform leverages pre-defined industrial expertise to identify limited subsets of available industrial data deemed relevant to a desired business objective, and to collect and model this relevant data to apply useful constraints on subsequent artificial intelligence or machine learning analytics applied to the data. This approach can reduce the data space to which AI analytics are applied, and assist data analytic systems to more quickly derive valuable insights and business outcomes. In some embodiments, the smart gateway platform can operate within the context of a multi-level industrial analytic system, feeding pre-modeled data to one or more AI or machine learning systems executing on one or more different levels of an industrial enterprise.

An I/O server service interacts with multiple containerized controller services each implementing the same control routine to control the same portion of the same plant. The I/O server service may provide the same controller inputs to each of the containerized controller services (e.g., representing measurements obtained by field devices and transmitted by the field devices to the I/O server service). Each containerized controller service executes the same control routine to generate a set of controller outputs. The I/O server service receives each set of controller outputs and forwards an “active” set to the appropriate field devices. The I/O server service and other services, such as an orchestrator service, may continuously evaluate performance and resource utilization in the control system, and may dynamically activate and deactivate controller services as appropriate.

An information processing device keeps a plurality of sets of designing information each on a combination of a plurality of plant devices that execute an operation a plant and at least one control device that executes control on each of the plant devices, keeps operational past records with respect to each combination of a plurality of plant devices and a control device, extracts combinations of a plurality of plant devices and a control device that meet a designing condition on the plant based on the designing information, and outputs the extracted combinations of a plurality of plant devices and a control device based on the operational past records.

A method for maintaining a physical asset based on recommendations generated by analyzing operational data and a composite model of a plurality of models representing the physical asset includes analyzing an error indicator associated with the composite model to determine that the error indicator exceeds a tolerance level and increases a number of basis functions in the port-reduced static condensation reduced basis element approximation accordingly. The computing device receives, from a sensor associated with the physical asset, first operational data associated with a region of the physical asset and updates the model, the operational data including data relating to a measurement of temperature and to a pressure load. The computing device determines a structural response of the physical asset to the thermal load and to the pressure load, including a determination of a level of creep strain. The computing device provides a recommendation for maintaining the physical asset.

A smart gateway platform leverages pre-defined industrial expertise to identify limited subsets of available industrial data deemed relevant to a desired business objective, and to collect and model this relevant data to apply useful constraints on subsequent artificial intelligence or machine learning analytics applied to the data. This approach can reduce the data space to which AI analytics are applied, and assist data analytic systems to more quickly derive valuable insights and business outcomes. In some embodiments, the smart gateway platform can operate within the context of a multi-level industrial analytic system, feeding pre-modeled data to one or more AI or machine learning systems executing on one or more different levels of an industrial enterprise.

“Cloud” based manufacturing execution systems (“MES”) and methods thereof used to control, execute, and monitor pharmaceutical or biopharmaceutical production processes and systems are disclosed herein. Consequently, the methods and systems provide a means to quality manufacturing on an integrated level whereby drug or biologic manufacturers can achieve data and product integrity and ultimately minimize cost.

A method for operating an industrial control system that has an automation controller with a sequential program, an actuator which actuates a switching component of the power electronics, and an input module, wherein activation and deactivation operations of the switching component cause electromagnetic interference that corrupts a measured value recorded via the input module, where a temporal occurrence of the activation and deactivation operations and/or an operating state is predicted for the switching component, and where the prediction is used to perform a correction of the measured value at a prediction time instant or during a prediction time range with respect to the corruption caused by the electromagnetic interference.

An industrial gateway device supports interactive feature engineering tools that guide a user through an intuitive process for configuring data analytics for key performance indicators (KPIs) of interest. A feature engineering interface renders an interactive model view that displays available data points such that the data points are organized hierarchically according to plant, machine, machine property, or other elements. The user can select, from this model view, data points having an impact on the KPI of interest. The interface also allows the user to define an executable script that defines a mathematical relationship between the selected data points and the KPI. This configuration yields an output model that defines a reduced set of data points to be collected and analyzed, as well as an executable script for assessing a state of the KPI as a function of the reduced data point values.

An electronic device and a method of filtering dispatching rules for a workstation are provided. The method includes the following. A dispatch rule set including a first dispatching rule and a first test data set are obtained. An initial individual is generated according to the dispatch rule set based on a genetic algorithm. A first individual is generated according to the first test data set and the initial individual based on the genetic algorithm, and the first individual includes a first dispatching rule sequence corresponding to a first workstation, and the first dispatching rule sequence includes a first dispatching rule. A first score corresponding to the first dispatching rule is calculated according to a first order of the first dispatching rule in the first dispatching rule sequence, and the dispatching rule set is updated according to the first score. The dispatching rule set that is updated is output.

An industrial gateway device supports interactive feature engineering tools that guide a user through an intuitive process for configuring data analytics for key performance indicators (KPIs) of interest. A feature engineering interface renders an interactive model view that displays available data points such that the data points are organized hierarchically according to plant, machine, machine property, or other elements. The user can select, from this model view, data points having an impact on the KPI of interest. The interface also allows the user to define an executable script that defines a mathematical relationship between the selected data points and the KPI. This configuration yields an output model that defines a reduced set of data points to be collected and analyzed, as well as an executable script for assessing a state of the KPI as a function of the reduced data point values.