Siemens Aktiengesellschaft

Siemens Corporation

Siemens Energy, Inc.

Siemenstechnology and Services Pvt. Ltd.

Anant Kumar Mishra

Himanshu Bhatnagar

Vinay Ramanath

Naveen Kumar Singa

Mayuri Deshpande

Martin Otto

Tobias Keute

Christopher Bachman

Joseph Kernich

A method for providing cybersecurity to an industrial system having multiple components includes monitoring communications across the components of the system and detecting a deviation in an expected communication state in a first component. Upon detection of a deviation, communication states of at least one other component in the system is observed. If a deviation is also detected in the other component, then the deviation is deemed to be unauthorized. If no other deviations are detected in other components, the original deviation is deemed to be benign. Detecting a deviation includes comparing an expected communication state based on a baseline to the current communication state, the communication state may be based on a communication's content or a pattern of communication actions.

Decision support for anomaly detection via impact analysis for security and safety

A computing system may include a fail-safe learning engine configured to access camera data captured by multiple cameras positioned within an environment during a learning phase, generate training data based on the camera data captured by the multiple cameras, and construct a human detection model using the training data. The computing system may also include a fail-safe trigger engine configured to access camera data captured by the multiple cameras positioned within the environment during an active phase, and the camera data captured during the active phase may include a target object. The fail-trigger engine may further be configured to provide, as an input to the human detection model, the camera data that includes the target object and execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

Machine learning-based environment fail-safes through multiple camera views

Methods and systems are provided for evaluating robustness of engineering components. An example method includes receiving an engineering problem definition for a multidimensional engineering domain of an engineering component, in which the engineering problem definition includes a description of a plurality of engineering features of the engineering component. Executing an optimal stratified sampling algorithm that obtains a plurality of probabilistic samples from the multidimensional engineering domain. Selecting optimal combinations of higher fidelity models, reduced order models and response surfaces for execution of the probabilistic samples. Executing the probabilistic samples using the selected combinations to determine a respective engineering response for each engineering feature of the plurality of engineering features. Generating a robustness scorecard for the engineering component based on the determined engineering responses to quantify a robustness score for each engineering feature of the plurality of engineering features. Presenting the robustness scorecard to a user.

Systems and methods to ensure robustness for engineering autonomy

Technical solutions are described for performing sensitivity analysis for engineering systems in spatial-temporal domain. An example method includes receiving a set of process parameters and retrieving a multidimensional dataset containing historical values of the process parameters and corresponding output values. The method further includes selecting a sampling algorithm to divide the multidimensional dataset into multiple subspaces, and selecting multiple samples (x), one sample from each subspace. The method further includes perturbing the samples, computing a first effect (EE) on an output value (y), and computing a second effect (SEE) of perturbing a pair of samples (xand x) on the output value. The method further includes computing a sensitivity coefficient of the process parameters on the output value using the second effect for xand x, the first effect for x, and the first effect for x. An automatic visualization scheme for the global sensitivity results is also provided.

Engineering automation using quantification and visualization of global sensitivities in spatial temporal domain

Method and apparatus to provide protocol translation and selectable data exchange in a client/server system are provided. A tag list is extracted from a legacy client deviceconnected to a legacy serverusing a protocol corresponding to the legacy server. A configuration manager deviceis used to configure the extracted tag list to obtain a selected tag list excerpt of the extracted tag list. The configuring device is arranged to map the selected tag list excerpt to a configuration adapted for a respectiveserver, and to define contextualization. Serverprovides industrial automation services using a next-generation protocol, e.g., OPC UA or MTConnect. A tag list is generated to configure server. A stream of data points of the selected tag list excerpt of the tag list extracted from the legacy client device is transferred to one or more client devicesconnected to server

Method and apparatus for protocol translation and exchange of selectable, contextualized data between a server using a next-generation protocol and a legacy server

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Charlotte, NC, United States of America

Anant Kumar Mishra