A building system for implementing building applications includes one or more memory devices storing instructions, the instructions causing one or more processors to receive a static semantic building application, wherein the static semantic building application comprises one or more operations and a first semantic description of the static semantic building application and determine, based on building information describing a building and the first semantic description of the static semantic building application, one or more implementation details of a particular implementation of the static semantic building application. The instructions cause the one or more processors to generate, based on the one or more implementation details, a dynamic semantic building application comprising the one or more operations and a second semantic description of the dynamic semantic building application and operate the dynamic semantic building application.

A method for controlling a charging station for charging vehicles in which a first charging unit of the charging station is coupled to a second charging unit of the relevant vehicle for transmitting current. When one of the vehicles is charged, an update operation of the charging station is performed, in which a control assembly of the one vehicle transmits a current software packet to a control device of the charging station.

An engineering apparatus according to the present disclosure generates generating executable code, which causes target hardware to operate, from a control application. The engineering apparatus includes an algorithm converter that converts control logic included in the control application into control logic code, a type management unit that outputs a type definition code corresponding to a data block structure of data held by a function block included in the control application, an instance management unit that outputs a memory allocation code that allocates an instance of the function block to memory, and a build controller that generates the executable code based on the control logic code, the type definition code, and the memory allocation code. Executable code for execution by target hardware is debugged while the executable code is in the form of a control application before conversion to a high-level language.

In the field of process automation there exists one or more custom solutions which are not scalable and missing essential needs to automate a process. This disclosure addresses the problem of codifying a process to automate by a declarative approach. A method for declarative modeling of process for automation is provided. The method includes receiving, request for automating the process; extracting, at least one feature associated with the process to obtain a declarative model; validating, the declarative model of the process with a process automation code to obtain a process automation package; provisioning, the declarative model to access and obtain specific roles associated with the process; executing, the process automation package in a process automation platform based on the specific roles to obtain a plurality of states; and monitoring, the plurality of states and triggers at least one action associated with the plurality of states in the process automation platform.

An industrial integrated development environment (IDE) supports commissioning features that facilitate intelligent deployment of an automation system project to appropriate industrial devices (e.g., industrial controllers, drives, HMI terminals, etc.). In some embodiments, the industrial IDE system can generate validation checklists that can be used during commissioning to validate the system and manage project validation sign-off procedures. After commissioning of the system, the IDE system can also support a number of runtime monitoring features, including monitoring the automation system during operation and providing assistance with regard to detecting, predicting, and correcting maintenance issues.

An industrial integrated development environment (IDE) provides a development framework for designing, programming, and configuring multiple aspects of an industrial automation system using a common design environment and data model. Projects creating using embodiments of the IDE system can be built on an object-based model rather than, or in addition to, a tag-based architecture. To this end, the IDE system can support the use of automation objects that serve as building blocks for this object-based development structure. To ensure consistency within and between projects, as well as to ensure that a given industrial project is dynamically updated to reflect changes to an industrial asset's attributes (e.g., control code, visualization definitions, testing scripts, analytic code, etc.), embodiments of the IDE system can use automation object inheritance features to propagate changes made to an automation object definition to all instances of the automation object used throughout a control project.

Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

Systems and methods for detecting and predicting faults in an industrial process automation system use trend data to forecast alerts and allow action to be taken before a problem occurs. The systems and methods provide fault/failure predictions that improve over time as more empirical data is collected for a related set of system components. The systems and methods may identify relationships among the components of a process automation system; identify and collect changes to system configuration; identify and collect data to inform reliability and predictive models; develop a domain-specific predictive model for one or more components that allows for component-based failure or degradation prediction; develop a system-predictive model that leverages reliability and criticality relationships, component-based predictions and operating parameters to predict the health of a part of or the entire process automation system; deliver a prioritized alert system; and identify root-cause failures of a component.

Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

A method comprising inputting a field development plan into a human interface, transferring the field development plan to an information handling system, transferring on or more electronic inputs into the information handling system, running a software program on the information handling system that integrates the electronic inputs with the field development plan to produce an output, sending the output to one or more pieces of equipment, operating the one or more pieces of equipment based at least in part on the output, measuring the operation of the one or more pieces of equipment with one or more sensors, sending the measurements from the one or more sensors back to the information handling system, running the software program with the measurements, updating the output based at least in part on the measurements and the field development plan, and sending an updated output to the one or more pieces of equipment.