Siemens Aktiengesellschaft

Siemens Energy, Inc.

Tao Cui

Justinian Rosca

Heiko Claussen

Joshua S McConkey

Lingyun Wang

Juan L Aparicio Ojea

Chengtao Wen

Hasan Sinan Bank

Martin Sehr

Mathias Maurmaier

Zainul Momin

Fernando Garcia-Perez

Phani Ram Kumar Kuruganty

Mohamed El Amine Houyou

Alessandra Oliveira Da Silva

Günter Struck

A system for processing data in an industrial environment includes a sensor that communicates sensor data acquired from a factory floor as a multicast data stream where each frame is tagged with a sequence identifier indicative of a position of that frame in the data stream. A network switch distributes the data stream to a group of multicast endpoints. The system includes a controller with a plurality of modularly connected processing units. Each processing unit is further connected to the network switch and configured as a respective endpoint of the group of multicast endpoints. Each processing unit selectively processes frames of the data stream in dependence of the sequence identifier of individual frames by executing a data processing module, to produce an output result associated with each processed frame. The controller assembles the output result and the sequence identifier of each processed frame from the plurality of processing units.

System and method for enabling scalable processing throughput of high-volume data on industrial controllers

A method of determining a turbine inlet temperature for a gas turbine engine includes measuring pressure changes within a combustion section of the gas turbine engine during operation of the gas turbine engine to produce pressure versus time data, extracting a resonant frequency from the pressure versus time data, and calculating the turbine inlet temperature based solely on the resonant frequency.

Turbine inlet temperature calculation using acoustics

Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.

Industrial process system threat detection

A sensing system is provided. The sensing system includes a processor, a sensor node, a plurality of sensors, a power source and a controller. The sensor node is operably connected to the processor on an industrial side and to the sensors on a sensor side. The sensors each measure a parameter of the sensing system and transmit the parameter to the processor via the sensor node. The power source delivers power to the processor. The controller is in operable communication with the processor. The sensor node, disposed in close proximity to the sensors, converts a data protocol for each of the sensors to a different industrial protocol and transmits the converted data through a communication port via the industrial data protocol to the processor. The processor then collects the measurements of each parameter of the plurality of sensors, analyses the measurements and transmits the analyzed data to the controller.

Universal high redundancy sensor interface for low cost sensing

Systems, techniques, and computer-program products are provided for tracking and traceability of parts of a finished product. In some embodiments, the tracking and traceability generates streams of semantic data obtained from an imaging sensor system that records the execution of a manufacturing process in industrial equipment. The execution of the manufacturing process yields a finished product from initial materials and/or parts. The tracking and traceability also implements artificial reasoning about the execution of the manufacturing process to generate assertions that characterize the execution of the manufacturing process. Semantic data and assertions can be aggregated into a digital trace record that tracks a defined component of the finished product throughout the execution of the manufacturing process and permit tracing the component to a defined event within the manufacturing process.

Tracking and traceability of parts of a product

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Princeton Junction, NJ, United States of America

Tao Cui