Siemens Aktiengesellschaft

Steffen Lamparter

Justinian Rosca

Thomas Hubauer

Lingyun Wang

Raffaello Lepratti

Mikhail Roshchin

Ingo Thon

Jan-Gregor Fischer

Michal Skubacz

George Lo

Silvio Becher

Michael Pirker

Martin Ringsquandl

Maja Milicic Brandt

Joachim Bamberger

Dragan Obradovic

Hartmut Ludwig

Michael Metzger

Juan L Aparicio Ojea

Markus Michael Geipel

Erwin Hess

Dagmar Beyer

Felix Buggenthin

Vladimir Zahorcak

Stephan Grimm

Kai Wurm

Mitchell Joblin

Gunther Birk

Sebnem Rusitschka

Gisbert Lawitzky

Ulli Waltinger

Rainer Krebs

Josep Soler Garrido

Emmanuel Bisse

Enrique Gaxiola

Stuart Watson

Matthias Loskyll

Lars Jordan

Clemens Hoffmann

Tobias Jäger

Christoph Legat

Ralf Hansen

Georg Heinecke

Michael Brucksch

Brent Hanniman

Jens Meckel

Nina Solomakhina

Thomas Bopp

Thomas Creutznacher

Alla Heidenreich

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.

Method and platform for deployment of an industrial application on an edge computing device of a machine tool

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

Method and system for automatic maintenance of a machine

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).

Method for production planning

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.

Method and system for detection of an abnormal state of a machine using image data and artificial intelligence

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.

Smart embedded control system for a field device of an automation system

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.

Method, field device and cloud infrastructure for data acquisition

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.

Method and system for query answering over probabilistic supply chain information

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.

Controlling methods, control systems, and plants using semantic models for quality criteria or adaptation of control rules

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

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.

Suggesting and/or creating agents in an industrial automation system

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.

Method and apparatus for operating an automation system and accounting for concept drift

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.

Method for planning the manufacture of a product and production module having self-description information

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.

Method for manufacturing a product according to a production plan

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.

Control contextualization and reasoning about control

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.

Generating events using contextual information on an intelligent programmable logic controller

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Feldkirchen, Germany

Steffen Lamparter