Siemens Aktiengesellschaft

Ingo Thon

Steffen Lamparter

Johannes Frank

Michal Skubacz

Heiko Claussen

Hans-Georg Köpken

Thomas Hubauer

Mikhail Roshchin

Felix Buggenthin

Andrés Botero Halblaub

Tim Schenk

Josep Soler Garrido

Serghei Mogoreanu

Mitchell Joblin

Marcel Hildebrandt

Maja Milicic Brandt

Andreas Macher

Tobias Jäger

Yi Huang

Christoph Wincheringer

Norman Drews

Renè Fischer

Georgia Olympia Brikis

Martin C Müller

Kiryl Batsiukov

A method of communicating with a microservice in an industrial facility. The method is implemented by a controller and includes obtaining one or more data structure types, wherein the data structure types are generated based on an API definition associated with the microservice, generating one or more variables based on the obtained data structure types, wherein the variables are associated with one or more function blocks of the controller, and transmitting data associated with the variables to the microservice via a proxy component. The controller transmits the data associated with the variables in a serialized binary format associated with the variables to the proxy component. The proxy component is configured to translate to translate the data from the serialized binary data to a first serialization format and transmit the data in the first serialization format to the microservice.

Method of communicating with a microservice in an industrial facility

An extension device for one or more automation devices in an industrial system is provided. Industrial data processing units capable of performing data processing based on one or more artificial neural networks are provided. To enable and/or accelerate one or more computations in an industrial system, thereby simplifying integration of artificial intelligence into the industrial system, and to simplify data exchange between an extension device capable of processing data using artificial intelligence and an automation device, one or more results of the one or more computations are obtained. The results indicate one or more states of the industrial system. The one or more results are provided via a process state model shared with the automation device to monitor and/or control the industrial system.

Extension device for an automation device

A computer-implemented method for efficient processing of pooled data shared by users of a cloud platform, the method includes the steps of uploading at least one dataset by a client device of a user to said cloud platform; calculating similarity scores indicating a degree of similarity between the current uploaded dataset and datasets previously uploaded by client devices of other users; and performing a procedure selected by a user on the cloud platform based on pooled data including the current dataset of the respective user and the datasets previously uploaded from client devices of other users stored in a database of the cloud platform having calculated similarity scores in relation to the current uploaded dataset of the respective user exceeding a configurable similarity score threshold, is provided.

Cloud platform and method for efficient processing of pooled data

A method includes determining an artificial intelligence function in an engineering framework system. An inference path is defined for generation of an AI model by a computation graph. An AI function and the inference path are converted into a processing format. The converted AI function is sent and exported to an extraction and extension module of an AI workbench module. The extended computation graph of the inference path is transmitted from the extraction and extension module to an AI framework module. The method includes communicating of a communication adapter with the processing module continuously by using a supporting communication protocol for receiving training data as input for the AI function and forwarding the training data to the AI framework module. Learned parameters of the AI model are transferred from an API interface of the AI framework module to the communication adapter for updating the AI model.

Method and system for generating an artificial intelligence model

Provided is a system and method for minimizing non-productive idle times within an automation process executed by an automation facility, the system including a model memory which stores a probabilistic model of a distribution of object exchange times of objects used or consumed in the automation process; and an optimizer adapted to calculate optimal assignments of objects to magazine positions of an object storage magazine depending on the probabilistic model and depending on a sequence of productive non-idle times of objects used or consumed in process steps of the automation process.

System and method for minimizing non-productive idle times within an automation process

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

A method for configuring an interface device connected to a control device and a field device, wherein the method includes receiving a first machine learning application having a plurality of logical components connected in a pipeline, where the first machine learning application serves to analyze a signal from the field device utilizing a first machine learning model, generating a plurality of code blocks utilizing a translator based on the plurality of logical components of the first machine learning application, connecting the plurality of code blocks in accordance with the pipeline of the first machine learning application to generate a first output from the signal from the field device, and deploying the connected code blocks on firmware of the interface device including creating a virtual port connectable to the control device, and where the virtual port serves to transmits the first output to the control device.

Interface device and method for configuring the interface device

The present disclosure provides an enhanced computation of a data model for an intelligent data processing device. The data processing device may be a device having limited computational resources. Accordingly, a system model for processing the data is computed in the local device. Additionally, an enhanced model may be computed in a remote device like a cloud or a data center. For this purpose, the cloud or datacenter is provided with filtered data for computing an enhanced model. The cloud or datacenter may compute an enhanced model and forward the respective model to the local device if the enhanced model is better than the model locally generated.

Data processing device, data analyzing device, data processing system and method for processing data

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

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

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.

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Grasbrunn, Germany

Ingo Thon