Abstract: A computer system and method for managing End-of-Life products is disclosed herein. The method includes generating, by a processor, a virtual representation of at least one part of the End-of-Life product based on inputs received from a source. Further, the virtual representation is processed to determine a part condition associated with the at least one part. Based on the part condition and at least one operational attribute associated with the at least one part, a health state of the at least one part is classified into at least one of a plurality of health states. Further, a circularity decision for managing the at least one part is identified from a knowledge graph, based on the health state of the at least one part. Based on the circularity decision identified, a recommendation for managing the at least one part is generated, on a graphical user interface.

Abstract: A system and method for determining optimal computing configuration for executing a computing operation includes defining one or more constrains of a given computing operation to be executed. The method further includes implementing a knowledge graph to determine at least one suitable combination of computing hardware and computing software based on the given computing operation and the defined one or more constrains therefor. The method further includes quantitatively estimating an energy requirement and qualitatively estimating an energy consumption pattern of the determined at least one suitable combination.

Abstract: A software-driven system is validated based on real-world scenarios in a Computer-Aided Engineering environment. A processor obtains a plurality of test scenarios that correspond to testing of the software-driven system. Further, at least one real-world scenario associated with the software-driven system is generated based on a set of variable parameters. Further, one or more test scenarios which are suitable for testing the software-driven system based on the at least one real-world scenario are identified from the plurality of test scenarios using a trained machine learning model. The identified test scenarios are applied on a model of the software-driven system in a simulated environment to evaluate a behaviour of the software-driven system. Based on an outcome of the evaluation, the behaviour of the software-driven system in the real-world scenario is validated.

Abstract: A system and method for determining optimal computing configuration for executing a computing operation includes defining one or more constrains of a given computing operation to be executed. The method further includes implementing a knowledge graph to determine at least one suitable combination of computing hardware and computing software based on the given computing operation and the defined one or more constrains therefor. The method further includes quantitatively estimating an energy requirement and qualitatively estimating an energy consumption pattern of the determined at least one suitable combination.

Abstract: A method is disclosed herein of detecting at least one frozen period in at least one sensor dataset associated with at least one sensor in a technical system. The method includes receiving the at least one sensor dataset in time series and computing run-lengths for the at least one sensor dataset, wherein each of the run-lengths is length of consecutive repetitions of a sensor value in the at least one sensor dataset. The method includes clustering the run-lengths into one of two clusters based on a run frequency, wherein the run frequency is a number of times the run-lengths are repeated in the at least one sensor dataset. Further, the method includes identifying a cluster from the two clusters with lower run frequency and detecting the at least one frozen period in the at least one sensor dataset based on the identified cluster.

Abstract: A method and system for generating a design of a product is provided. The method includes obtaining a model of the product. The model is associated with a design of the product. The method includes simulating a model of the product with respect to an environmental sustainability of the product. The method includes generating simulation results indicative of behavior of the model with respect to the environmental sustainability. The simulation results comprise an environmental sustainability index of the product. The method includes determining whether the environmental sustainability index of the product satisfies an environmental sustainability threshold value. Further, the method includes generating a modified model of the product if the environmental sustainability index of the product satisfies the environmental sustainability threshold value.

Abstract: 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.

Abstract: 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 (xi), one sample from each subspace. The method further includes perturbing the samples, computing a first effect (EEi) on an output value (y), and computing a second effect (SEEii) of perturbing a pair of samples (xi and xj) 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 xi and xj, the first effect for xi, and the first effect for xj. An automatic visualization scheme for the global sensitivity results is also provided.

Abstract: In one example implementation according to aspects of the present disclosure, a computer-implemented method includes identifying, by a processing device, a transition feature of the object based at least in part on point cloud data corresponding to the object. The method further includes performing, by the processing device, a geometric analysis on the transition feature of the object based at least in part on a curvature deviation. The method further includes generating a fitted parametric surface for the object based at least in part on the transition feature of the object and results of the geometric analysis on the transition feature of the object.

Abstract: A method, a device, and a system of life estimation of a technical system including at least one material are disclosed. The method includes generating a coefficient distribution by determining a probability distribution of condition coefficients associated with the material. The condition coefficients include a stress-strain coefficient, a stress-life coefficient, and structure coefficients. The method also includes sampling the coefficient distribution at a high confidence region and a low confidence region. The life of the material is estimated based on the sampled high confidence region and the sampled low confidence region.

Abstract: A method is disclosed herein of detecting at least one frozen period in at least one sensor dataset associated with at least one sensor in a technical system. The method includes receiving the at least one sensor dataset in time series and computing run-lengths for the at least one sensor dataset, wherein each of the run-lengths is length of consecutive repetitions of a sensor value in the at least one sensor dataset. The method includes clustering the run-lengths into one of two clusters based on a run frequency, wherein the run frequency is a number of times the run-lengths are repeated in the at least one sensor dataset. Further, the method includes identifying a cluster from the two clusters with lower run frequency and detecting the at least one frozen period in the at least one sensor dataset based on the identified cluster.

Abstract: 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 (xi), one sample from each subspace. The method further includes perturbing the samples, computing a first effect (EEi ) on an output value (y), and computing a second effect (SEEii) of perturbing a pair of samples (xi and xj) 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 xi and xj, the first effect for xi, and the first effect for xj. An automatic visualization scheme for the global sensitivity results is also provided.

Abstract: 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.

Abstract: A method, device and system of estimation life of a technical system comprising of at least one material, is disclosed. The method includes generating a coefficient distribution by determining probability distribution of condition coefficients associated with the material. The condition coefficients include stress-strain coefficient, stress-life coefficient and structure coefficients. Further, the method includes sampling the coefficient distribution at a high confidence region and a low confidence region. The life of the material is estimated based on the sampled high confidence region and the sampled low confidence region.

Abstract: A system, device, and method of deviation detection in at least one sensor dataset associated with one or more sensors in a technical system are provided. The method includes generating a best fit model of the technical system based on a target sensor dataset. The method also includes predicting a sensor dataset of the target sensor using the best fit model and non-target sensor datasets of non-target sensors, and determining a deviation tolerance by determining a difference between the predicted sensor dataset and the target sensor dataset. The method also includes detecting deviation in actual sensor dataset of the target sensor when a data-point in the actual sensor dataset exceeds the deviation tolerance and detecting deviation in the at least one sensor dataset of the one or more sensors by detecting deviation in each of the non-target sensor datasets.

Abstract: A system, device, and method of deviation detection in at least one sensor dataset associated with one or more sensors in a technical system are provided. The method includes generating a best fit model of the technical system based on a target sensor dataset. The method also includes predicting a sensor dataset of the target sensor using the best fit model and non-target sensor datasets of non-target sensors, and determining a deviation tolerance by determining a difference between the predicted sensor dataset and the target sensor dataset. The method also includes detecting deviation in actual sensor dataset of the target sensor when a data-point in the actual sensor dataset exceeds the deviation tolerance and detecting deviation in the at least one sensor dataset of the one or more sensors by detecting deviation in each of the non-target sensor datasets.