Abstract: Provided is a deployment platform for deployment of an industrial application on an edge computing device, ECD, connected to the controller of a machine tool, MT, the deployment platform including a model management component, MMC, adapted to instantiate a generic machine tool model, GMTM, stored in a memory to provide a machine instance model, MIM, of the respective machine tool, MT, on the basis of a machine tool data report, MTDR, received by the model management component, MMC, from the edge computing device, ECD, of the respective machine tool, MT, and further adapted to convert generic data requirements, gR, of a generic industrial application into machine tool specific requirements, mtsR, using the machine instance model, MIM, of the respective machine tool, MT, wherein an instantiated industrial application for the machine tool, MT, is provided by instantiating the generic industrial application by extending its configuration data with machine tool specific requirements.

Abstract: Various embodiments of the teachings herein include a system for automatically synchronizing and providing data for retrieval by intelligent machines and/or users. For example, the system may include: a computing unit providing a platform; an input and output unit; and an artificial intelligence passively monitoring the input and output unit and learning from the responses, adaptations and/or assessments of the user. The artificial intelligence automatically carries out synchronization, in line with the input, of the various data types, metadata and/or memory levels of the inputs and/or outputs, develops search strategies and/or makes search results available in the output unit.

Abstract: To create or update a digital twin modeling a physical system, data from several conjoint data sources are repeatedly extracted and semantically annotated, wherein semantic annotations of data that have been extracted from a first conjoint data source are influenced by previously extracted and semantically annotated data from at least one other conjoint data source. The semantically annotated data are used to create or update the digital twin. This provides a modular and easily expandable solution for enriching a digital twin from a plurality of conjoint data sources with heterogenous data. Processing of different data sources does not happen isolated from one another while the system remains modularly expandable to new data sources. Compared to previous approaches, instead of creating yet another stand-alone digital representation of the physical system that is not compatible with already existing solutions, these solutions are augmented with a modular data management pipeline.

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: 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 for generating respective self-description data modules with respect to at least one functionality and/or at least one component, device, or system, wherein a data collection is provided comprising engineering elements from at least one of the three data sources, where relationship information is ascertained in which each piece of relationship information is assigned to engineering elements of the data collection, a multigraph comprising nodes and connections between nodes is generated, a clustering method is applied to the multigraph to ascertain at least one sub-graph within the multigraph, a sub-graph is selected from the ascertained at least one sub-graph, and a self-description data module relating to a component and/or a functionality of the device or system is generated.

Abstract: A method for generating a digital twin of a system or device includes identifying component data clusters within the first data source, where the component data clusters are assigned or assignable component types or component ID information relating to the system or device, allocating a respective component type designation or a respective component ID information designation to at least one of the identified component data clusters, and generating and storing the digital twin of the system or device.

Abstract: A system and method for generating a holistic digital twin of an industrial facility that includes a plurality of assets, wherein the said system includes conversion units configured to convert asset related data collected from different tools utilized to plan and/or operate the industrial facility in a tool specific data format and asset related data provided by data sources of the industrial facility in a data source specific data format into a common graphical representation, a matching unit configured to match the common graphical representations of the converted asset related data to provide a mapping between the assets of the industrial facility, and a merging unit configured to merge the mapped assets of the industrial facility into a unified graph to provide the holistic digital twin of the industrial facility.

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: An embedded control system for a field device of an automation 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; and 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 sensor data are compressed on field devices using a representation is provided. The field device immediately decompresses the compressed data in order to detect a deviation. If there is a deviation, then a cloud storage receives the sensor data as raw uncompressed data. A cloud component receives a trigger signal from the field device, indicating that the representation used by the field device for compression does not sufficiently describe the sensor data. The cloud component then learns a new representation by retrieving and analyzing all data stored in the cloud storage. The method and field device provide robust, compression-based data acquisition. They improve quality and precision of the data captured by the field devices. As the representation in the field device can be updated, it becomes possible to accommodate changes in the device setup. The cloud infrastructure provides automatic learning of the representation in the cloud.

Abstract: A method predicts a derivable event in a logistic network. The method includes generating a Bayesian network describing a structure of at least a part of a logistic network. A query is received for the derivable event that depends on a combination of base events in the logistic network. The Bayesian network is instantiated for a plurality of points in time. A prediction of the derivable event is deduced from the instantiated Bayesian network by use of complex event processing.

Abstract: Hidden Features are locally extracted from Industrial Data of the industrial system by a Local Application executed on a local computer of a customer. The Hidden Features are uploaded to an external computer of a service provider. A Domain Model for the industrial system is externally determined from an Industrial Model Library (IML) on the external computer based on the uploaded Hidden Features by an External Algorithm including at least one Machine Learning Model (MLM) executed on the external computer. The determined Domain Model for the industrial system is provided to the customer. The at least one MLM has been trained on ranking most appropriate Domain Models for industrial systems based on Hidden Features of the respective industrial systems. The most appropriate Domain Models represent all relevant technical aspects of the respective industrial systems.

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: 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.

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: Provided is a deployment platform for deployment of an industrial application on an edge computing device, ECD, connected to the controller of a machine tool, MT, the deployment platform including a model management component, MMC, adapted to instantiate a generic machine tool model, GMTM, stored in a memory to provide a machine instance model, MIM, of the respective machine tool, MT, on the basis of a machine tool data report, MTDR, received by the model management component, MMC, from the edge computing device, ECD, of the respective machine tool, MT, and further adapted to convert generic data requirements, gR, of a generic industrial application into machine tool specific requirements, mtsR, using the machine instance model, MIM, of the respective machine tool, MT, wherein an instantiated industrial application for the machine tool, MT, is provided by instantiating the generic industrial application by extending its configuration data with machine tool specific requirements.

Abstract: A method and an apparatus for operating an automation system is provided. The method for operating an automation system includes the method steps of: providing a learning-based prediction model for the automation system trained by process data including context of an automation process, receiving information about current context of the automation process, verifying context change by comparing the current context to the context of said process data, in the case of any context change verifying a concept drift by comparing pre-drift process data and post-drift process data, in the case of any concept drift re-training said model with post-drift process data, in the case of no context change testing for random concept drift not detected by verifying context change, in the case of any random concept drift extend the current context by using data comprising previous context changes, otherwise no further method steps are required.

Abstract: A production module for performing a production function on a product, production system, production planning device, and method for planning the production of the product, wherein a plurality of production modules are intercoupled, where a self-description information set is stored within each production module as a database, e.g., NoSQL, OWL, ontology, SPARQL, which comprises properties of the production module, where if a production information set comprising the production steps required to produce the product is present, then the production information set and the self-description information sets or parts thereof are transmitted to a production planning device to plan production of the product and a production procedure plan for a product to be processed is determined, and where the production procedure plan comprises an information set about a sequence of production modules of the production system, which sequence a product should pass through to produce an intermediate product or end product.

Abstract: A sensor data are compressed on field devices using a representation is provided. The field device immediately decompresses the compressed data in order to detect a deviation. If there is a deviation, then a cloud storage receives the sensor data as raw uncompressed data. A cloud component receives a trigger signal from the field device, indicating that the representation used by the field device for compression does not sufficiently describe the sensor data. The cloud component then learns a new representation by retrieving and analyzing all data stored in the cloud storage. The method and field device provide robust, compression-based data acquisition. They improve quality and precision of the data captured by the field devices. As the representation in the field device can be updated, it becomes possible to accommodate changes in the device setup. The cloud infrastructure provides automatic learning of the representation in the cloud.