Siemens Aktiengesellschaft

Stephan Grimm

Maja Milicic Brandt

Justinian Rosca

Lingyun Wang

Steffen Lamparter

George Lo

Thomas Hubauer

Mikhail Roshchin

Sonja Zillner

Dagmar Beyer

Martin Ringsquandl

Marcel Hildebrandt

Sebastian Albrecht

Jens Schnittger

Daniel Meyer-Delius Di Vasto

Stuart Watson

Christian Seitz

Michael Watzke

Holger Stender

Falco Riccardo Cescolini

Anna Himmelhuber

Alois Haselböck

Rainer Schiekofer

Yushan Liu

Lisa Theresa Abele

Richard Taupe

Production logs and industrial ontologies are processed with an inductive logic program performing class expression learning in order to create class expressions, with each class expression representing a constraint or property of a skill of a production module. The resulting class expressions are ordered by a metric to form an ordered recommender list and displayed to a user for postprocessing. The user selects suitable class expressions from the ordered recommender list, so that the system can build a machine-readable skill description with the selected class expressions. This approach to generating formal, machine-readable skill descriptions minimizes the labor time and domain expertise needed to equip production modules with their skill description. Selecting the correct class expression from the automatically generated ordered recommender list is a much lower effort than manual labeling from scratch.

Method and system for semi-automated generation of machine-readable skill descriptions of production modules

Providing data generally involves a transformation of an OPC UA data model into an ontology. Such an ontology readily provides desired capabilities for validation, querying, and analytics of the OPC UA data model using sophisticated standard tools adapted to the ontology. A method for transforming a semantically enriched and graph-based data model into a target ontology is suggested. By a first act, identifications of nodes are retrieved of the data model. Subsequently or concurrently, semantic descriptions of one or more references interconnecting one or more nodes are retrieved. These semantic descriptions are expressed by at least one or more predicates and one or more concepts (e.g., classes) in the target ontology to be produced. Eventually, the target ontology is structured by a semantic ontology language and output to a triple store.

Method for transforming a data model for automation purposes into a target ontology

A production support system for generating a plant bill of process, PBoP, from a product bill of process, PdBoP, for a production plant, a corresponding method and a corresponding computer program product is provided. The production support system includes: (a) a reasoning component for determining possible cells; (b) a selection component for selecting all those cells that address operation capabilities used in the PdBoP; (c) an operation capabilities identifying component for identifying, additional operation capabilities for the selected cells which include at least two production entities, PET, based on the operation capabilities of the individual production entities, PET; and (d) a PBoP generating component for generating the PBoP from the PdBoP using the additional operation capabilities identified by the operations identifying component.

System and method for generating a plant bill of process

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.

Rich contextualization of automation data

The disclosure relates to a method for checking whether and/or how a technical process may be carried out using a technical system with two or more units having technical capabilities that are potentially relevant to the technical process. At least one ontology is used which describes the technical system and the technical process, and a constraint satisfaction problem is generated using the at least one ontology. In the method, solutions are found for the constraint satisfaction problem. The solutions may be stored or output.

Checking method, device, and computer program product

A method and apparatus for actuating a machine is provided in which a relaxed abduction problem is determined in order to explain the greatest possible part of the observations with the fewest possible assumptions. Based upon two preference orders over a subset of observations and a subset of assumptions, tuples are determined so that the theory together with the subset of assumptions explains the subset of observations. On the basis of the formal validity of the approach certain characteristics of the set of results (such as correctness, completeness, etc.) are checked. By the choice of underlying representational language and the preference relations, the complexity of the problem-solving process is influenced and thus flexibly adapted with regard to domain requirements. The invention may be used for any machines, e.g. gas turbines or steam turbines.

Control of a machine using relaxed abduction method

A method is disclosed for operating a programmable logic controller which executes program(s) based on cycles having a predetermined cycle time in a predefined memory. An automated reasoning method based on an ontology and a description logic is implemented in the programmable logic controller. To enable this, at least the concepts and roles of the ontology are encoded using index numbers and the axioms of the ontology are encoded using tuples of integral index numbers. The automated reasoning method is interrupted before the end of a cycle and subsequently resumed at the current status of program execution. This allows for the cyclic programming paradigm of a programmable logic controller. The method can be used for any types of programmable logic controller, e.g., controllers of devices belonging to an automation system or energy generation devices. In such scenarios the automated reasoning method may be used for diagnosing the corresponding devices.

Method for encoding ontology reasoning on a programmable logic controller

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Munich, Germany

Stephan Grimm