Abstract: An object recognition apparatus for automatic detection of an abnormal operation state of a machine including a machine tool operated in an operation space monitored by at least one camera configured to generate camera images of a current operation scene is provided. The generated camera images are supplied to a processor configured to analyze the current operation scene using a trained artificial intelligence module to detect objects present within the current operation scene. The processor is also configured to compare the detected objects with objects expected in an operation scene in a normal operation state of the machine to detect an abnormal operation state of the machine.

Abstract: A method for manufacturing a product according to a production plan includes a plurality of production steps. The method includes providing a plurality of production modules, for each production step of the plurality of production steps, independently executing a negotiation process for selecting a production module, and disposing the selected production modules, each selected for performing one of the production steps, for performing the plurality of production steps. The negotiation process includes designating production modules that are capable of performing the production step, assigning module parameters to the designated production modules, calculating a cost for each of the designated production modules based on the module parameters of the respective production module, and selecting one of the designated production modules as a function of the calculated cost.

Abstract: A method of operating an intelligent programmable logic controller includes executing, by a processor included in the intelligent programmable logic controller, a control program. A non-volatile storage medium included in the intelligent programmable logic controller stores a control model comprising control knowledge related to the control program. The control model is used to perform a monitoring operation related to execution of the control program on the intelligent programmable logic controller.

Abstract: A method of generating events based on automation system data in an intelligent programmable logic controller includes the intelligent programmable logic controller collecting automation system data and applying a context model to the automation system data to create contextualized data. The intelligent programmable logic controller generates one or more events based on the contextualized data.

Abstract: The invention relates to a production planning method using a plurality of manufacturing devices (INTMA) according to which tasks (TD) of a work plan (BOP) are compared (MA) with manufacturing capabilities (SD) of the manufacturing devices (INTMA) and, depending on the one or more results (MAQ) of said comparison (MA), at least one or more manufacturing devices (INTMA) are commissioned to match their manufacturing capabilities (SD) with the task(s) (TD).

Abstract: A method and system for automatic maintenance of a machine (2) comprising the steps of receiving (S1) at least one maintenance relevant event (E) from a controller (3) of the machine (2); augmenting (S2) the received event (E) with the event's machine context read from a machine maintenance ontology; matching (S3) the event's machine context with maintenance rules to generate at least one maintenance task (T) comprising an associated task description; and providing (S4) a maintenance schedule for the machine (2) assigning the generated maintenance task (T) to suitable maintenance executing entities (5) on the basis of the task description of the respective maintenance task (T).

Abstract: A computer-implemented method for detecting faults and events related to a system includes receiving sensor data from a plurality of sensors associated with the system. A hierarchical failure model of the system is constructed using (i) the sensor data, (ii) fault detector data, (iii) prior knowledge about system variables and states, and (iii) one or more statistical descriptions of the system. The failure model comprises a plurality of diagnostic variables related to the system and their relationships. Probabilistic reasoning is performed for diagnostic or prognostic purposes on the system using the failure model to derive knowledge related to potential or actual system failures.

Abstract: A control apparatus of an automation system, the control apparatus includes a database adapted to store time series data in a historian data source and adapted to store events derived from the time series data based on event detection rules in an event data source, wherein a semantic data or event query received by the control apparatus is mapped to a corresponding data source of the database to retrieve the queried data or event which are contextualized using an ontological context model of the automation system stored in the database and output by control apparatus in a semantic format is provided.

Abstract: A configuration system and method adapted to perform a configuration or reconfiguration of applications run by an automation system, the configuration system including a processing unit adapted to process at least one natural language statement of a user requirement input by a user concerning a control and/or monitoring functionality of the automation system based on a user ontology of the user and/or an automation system ontology of the automation system to generate a formal requirements specification, and a matching unit adapted to match the generated formal requirements specification and formal component specifications read from a component library to derive a configuration deployment including one or several suitable components with configurations fulfilling the input user requirement.

Abstract: A method of operating an intelligent programmable logic controller over a plurality of scan cycles includes the intelligent programmable logic controller executing a control program and one or more reasoning algorithms for analyzing data received and transmitted by the intelligent programmable logic controller. The intelligent programmable logic controller receives one or more user-specified declarative knowledge models from an external source via a deployment interface included in the intelligent programmable logic controller. The intelligent programmable logic controller dynamically modifies the reasoning algorithms during runtime of the control program based on the user-specified declarative knowledge models.

Abstract: In a monitoring phase, live instance vectors including data from all devices of a manufacturing system are acquired. A constraint-based clustering algorithm assigns each live instance vector to a cluster, thereby forming a live sequence of clusters. The live sequence is classified based on at least one behavior model. An anomaly is detected depending on the classification result. Each cluster represents a state of the manufacturing system. The sequences of clusters can be generated by consecutive operations that are performed in the manufacturing system. The constraint-based clustering algorithm facilitates an unsupervised (automated) or semi-supervised learning of system behavior that may be supplemented with supervised or unsupervised learning of the behavior models. The method provides a way of automated learning of discrete event dynamic systems from data generated by sensors and actuators without requiring manual input.

Abstract: A computer-implemented method for detecting faults and events related to a system includes receiving sensor data from a plurality of sensors associated with the system. A hierarchical failure model of the system is constructed using (i) the sensor data, (ii) fault detector data, (iii) prior knowledge about system variables and states, and (iii) one or more statistical descriptions of the system. The failure model comprises a plurality of diagnostic variables related to the system and their relationships. Probabilistic reasoning is performed for diagnostic or prognostic purposes on the system using the failure model to derive knowledge related to potential or actual system failures.

Abstract: The controlling method uses a control specification. In the method, at least part of the progression of the controlling process is monitored, and at least one quality criterion characterizing the quality of the control method is determined, (e.g., ascertained), in accordance with the progression. The control specification is adjusted in accordance with the quality criterion.

Abstract: It is proposed that an industrial installation, be operated with the power consumption being optimized for a target function. The target function may also factor in further parameters, with the result that multitarget optimization, for example, can be performed and the installation can be adapted in respect of the power consumption or the power consumption can be adapted in respect of the installation. Both the industrial installation and an energy supplier or network operator provide information which can be factored in as appropriate for the purpose of optimization or as part of the target function. It is advantageous that overload situations are avoided and a large number of regenerative energy sources can be used as energy suppliers, because adaptation is effected in line with the amount of energy actually provided. Hence the power supply system can be operated and loaded as appropriate.

Abstract: An embedded control system for a field device of an automation system is provided. The embedded control system includes: a diagnostic application interface to a backend server for signal analytics information, complex event pattern information, and diagnostic information; a physical process interface to a signal source for transferring signal data; a signal evaluation component for comparing received signal analytics information with received signal data to identify a first and a second event; an event processing component for applying received event pattern information to the first and second identified events to identify a first classified event; a diagnostic reasoning component for deriving causal dependencies between the first classified event and a further classified event with regard to diagnostic information to identify a root cause for the first classified event, or predict an impact of the first classified event.

Abstract: A method of operating an intelligent programmable logic controller includes executing, by a processor included in the intelligent programmable logic controller, a control program. A non-volatile storage medium included in the intelligent programmable logic controller stores a control model comprising control knowledge related to the control program. The control model is used to perform a monitoring operation related to execution of the control program on the intelligent programmable logic controller.

Abstract: An automation system, a method and an apparatus for suggesting and/or creating an agent in an industrial automation system that includes automation devices having a framework which is formed to execute the agent and which at least partially includes a data source that collects and/or processes data of the automation devices, and includes a data sink, in which data, in particular status data, of the data sources is saved, wherein an agent suggestion component processes data of the data sink into clusters via a cluster analysis, and wherein the agent suggestion component makes the clusters available at an interface such that a model for the agents becomes creatable by an agent generation component based on at least one selection of the clusters such that it becomes possible to suggest and/or create agents in a simpler and more efficient manner.

Abstract: A method of contextualizing automation system data in an intelligent programmable logic controller includes the intelligent programmable logic controller collecting automation system data and creating a structured representation of the automation system data. The intelligent programmable logic controller selects a semantic context model from a plurality of available semantic context models based on relevance to the structured representation of the automation system data and creates semantically contextualized data using the semantic context model and the structured representation of the automation system data. The semantically contextualized data is stored by the intelligent programmable logic controller on a non-volatile computer-readable storage medium operably coupled to the intelligent programmable logic controller.

Abstract: A method for manufacturing a product according to a production plan includes a plurality of production steps. The method includes providing a plurality of production modules, for each production step of the plurality of production steps, independently executing a negotiation process for selecting a production module, and disposing the selected production modules, each selected for performing one of the production steps, for performing the plurality of production steps. The negotiation process includes designating production modules that are capable of performing the production step, assigning module parameters to the designated production modules, calculating a cost for each of the designated production modules based on the module parameters of the respective production module, and selecting one of the designated production modules as a function of the calculated cost.

Abstract: A method of operating an intelligent programmable logic controller (PLC) as part of a production process within an automation system includes the intelligent PLC receiving automation system data and a semantic context model comprising a plurality of ontologies providing formal specifications of conceptual entities associated with the automation system. The intelligent PLC creates one or more semantic annotations for the automation system data using the semantic context model. These semantic annotations are stored along with the automation system data in a non-volatile storage medium included in the intelligent PLC.