Siemens Energy, Inc.

Siemens Aktiengesellschaft

Siemens Corporation

Siemenstechnology and Services Pvt. Ltd.

Himanshu Bhatnagar

Clifford Hatcher

Yakup Genc

Forrest R Ruhge

Richard Michael Hatley

Dennis H Lemieux

Anant Kumar Mishra

Vinay Ramanath

Kai Kadau

Chirag B Patel

Cristina C Popescu

Christopher Bachman

Tobias Keute

Thomas Koeppe

Jr Clifford Hatcher

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

A method for operating a machine component under stress. The method comprises determining a probability of failure PoF(N) of the component as a function of N cycles, selecting a time-based acceptable risk limit for the component and selecting an operational profile for the component, converting the time-based acceptable risk limit to a cycle-based acceptable risk limit using the operational profile, comparing the cycle-based acceptable risk limit with the PoF(N) values to determine an operational status of the component, comparing the cycle-based acceptable risk limit with the PoF(N) values, and operating the machine component responsive to results of the comparing step.

Scheduling inspections and predicting end-of-life for machine components

Internal components of power generation machinery, such as gas and steam turbines are inspected with an optical camera inspection system that is capable of automatically positioning the camera field of view (FOV) to an area of interest within the machinery along a pre-designated navigation path and capturing images without human intervention. Automatic camera positioning and image capture can be initiated automatically or after receipt of operator permission. The pre-designated navigation path can be defined by operator manual positioning of an inspection scope within the power machine or a similar one of the same type and recording of positioning steps for future replication. The navigation path can also be defined by virtual simulation.

System and method for automated optical inspection of industrial gas turbines and other power generation machinery

Systems and methods for inspecting a device are disclosed. The method includes arranging the device in a known position relative to a plurality of movable cameras. The plurality of movable cameras is mounted on a controllable actuator. The plurality of cameras is pointed at the device by controlling the controllable actuator to position the camera with a user interface. An image of the device generated by the camera is displayed on a mobile and wireless display. The computing device also causes a rendered virtual image of the device to be displayed on the mobile and wireless display. A stream of interest and a region of interest is selected at the mobile and wireless display from the images generated by the cameras.

Systems and methods for hands free inspection

Internal components of power generation machinery, such as gas and steam turbines are inspected with an optical camera inspection system that is capable of automatically positioning the camera field of view (FOV) to an area of interest within the machinery along a pre-designated navigation path and capturing images without human intervention. Automatic camera positioning and image capture can be initiated automatically or after receipt of operator permission. The pre-designated navigation path can be defined by operator manual positioning of an inspection scope within the power machine or a similar one of the same type and recording of positioning steps for future replication. The navigation path can also be defined by virtual simulation. The inspection system includes an articulated multi-axis inspection scope.

System and method for automated optical inspection of industrial gas turbines and other power generation machinery with articulated multi-axis inspection scope

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Charlotte, NC, United States of America

Himanshu Bhatnagar