Abstract: Monitoring an industrial process by building a training dataset of system data representative of status of industrial process parameters and training a custom query engine based on the training dataset. Models are generated using the custom query engine for matching query terms to the system data in response to user input representative of the system data that the user intends to access. Executing one of the models based on the input from the user generates an output retrieving the selected system data from the data tables for visualization.

Abstract: Monitoring an industrial process by building a training dataset of system data representative of status of industrial process parameters and training a custom query engine based on the training dataset. Models are generated using the custom query engine for matching query terms to the system data in response to user input representative of the system data that the user intends to access. Executing one of the models based on the input from the user generates an output retrieving the selected system data from the data tables for visualization.

Abstract: Monitoring an industrial process by building a training dataset of system data representative of status of industrial process parameters and training a custom query engine based on the training dataset. Models are generated using the custom query engine for matching query terms to the system data in response to user input representative of the system data that the user intends to access. Executing one of the models based on the input from the user generates an output retrieving the selected system data from the data tables for visualization.

Abstract: An alarm rationalization system receiving and responsive to industrial process information collected from a process control system for identifying one or more alarms and executing an artificial intelligence (AI) alarm engine. The AI alarm engine builds a process/domain model based on the received industrial process information and historized alarm information to evaluate the alarms in accordance with a predefined alarm philosophy. The AI alarm engine then generates a plurality of alarm definitions based on the model to optimize the alarms. The AI alarm engine automatically populates a Master Alarm Database (MADB) with the alarm definitions. The alarms are then rationalized based on the alarm definitions stored in the MADB.

Abstract: Configuring distributed control in an industrial system comprises building an asset model representative of a process control installation of the industrial system and creating an asset library of distributed control assets according to a distributed control programming standard. The asset model includes modeled assets defined according to levels of a physical model standard and representing physical devices of the industrial system. The distributed control assets each have one or more predefined, built-in facets. One of the distributed control assets in the asset library is mapped to each of the modeled assets to configure the process control installation of the industrial system and generate an asset-based control application for providing distributed control of the industrial system. Additional aspects relate to auto-creation of control applications based on an information model, either through the use of machine learning or an asset configurator tool.

Abstract: Configuring distributed control in an industrial system comprises building an asset model representative of a process control installation of the industrial system and creating an asset library of distributed control assets according to a distributed control programming standard. The asset model includes modeled assets defined according to levels of a physical model standard and representing physical devices of the industrial system. The distributed control assets each have one or more predefined, built-in facets. One of the distributed control assets in the asset library is mapped to each of the modeled assets to configure the process control installation of the industrial system and generate an asset-based control application for providing distributed control of the industrial system. Additional aspects relate to auto-creation of control applications based on an information model, either through the use of machine learning or an asset configurator tool.