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21.发明申请公开(公告)号：US20210357740A1
公开(公告)日：2021-11-18
申请号：US16337154
申请日：2018-04-12
Applicant: Siemens Aktiengesellschaft
Inventor： Xi He , Ioannis Akrotirianakis , Amit Chakraborty
Abstract: A computer-implemented method for training a deep neural network includes defining a loss function corresponding to the deep neural network, receiving a training dataset comprising training samples, and setting current parameter values to initial parameter values. An optimization method is performed which iteratively minimizes the loss function. During each iteration, a steepest direction of the loss function is calculated by determining the gradient of the loss function at the current parameter values. A batch of samples included in training samples is selected. A matrix-free CG solver is applied to obtain an inexact solution to a linear system defined by the steepest direction of the loss function and a stochastic Hessian matrix with respect to the batch of samples. A descent direction is determined, and the parameter values are updated based on the descent direction. Following the optimization method, the parameter values are stored in relationship to the deep neural network.
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公开(公告)号：US20210324835A1
公开(公告)日：2021-10-21
申请号：US17270468
申请日：2018-08-31
Applicant: Siemens Aktiengesellschaft
Inventor： Alexis Motto , Amit Chakraborty
Abstract: A method for identifying underperforming agents in a multi-agent cooperative system includes receiving information relating to the performance of each agent in the multi-agent system, calculating an estimated extracted resource value of each agent based on the received information, comparing the estimated extracted resource value of each agent to a threshold value, calculating a performance index based on the comparison and identifying an agent as an under-performing agent based on the performance index. A system for identifying under-performing agents in a plurality of agents in a multi-agent cooperative system includes a performance analyzing processor, a communications port for receiving state information for each agent and control information for each agent, a classifier for identifying a subset of agents in the plurality of agents that are performance comparable and an optimizer configured to identify an under-performing agent of performance comparable agents and generate updated control information for the identified under-performing agent.
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公开(公告)号：US20210279853A1
公开(公告)日：2021-09-09
申请号：US16810129
申请日：2020-03-05
Applicant: Siemens Aktiengesellschaft
Inventor： Arindam Dasgupta , Biswadip Dey , Anand A. Kulkarni , Amit Chakraborty
Abstract: A computer-implemented method for assessing material microstructure of a machine component involves obtaining a raw image of a section of the component captured via a microscope. The method further includes pre-processing the raw image to generate a ternary image defined by pixel data including three levels of intensities. The method further includes identifying, from the ternary image, phase boundaries delineating at a phase in a primary constituent material of the component. The method further includes determining a volume associated with the phase based on the identified phase boundaries. The proposed method may be utilized, for example, as an automated tool for assessing material degradation and for quality control of gas turbine engine components.
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公开(公告)号：US20210182296A1
公开(公告)日：2021-06-17
申请号：US17268619
申请日：2018-08-24
Applicant: Siemens Aktiengesellschaft
Inventor： Chao Yuan , Amit Chakraborty , Claus Neubauer
IPC: G06F16/2455 , G05B23/02
Abstract: Systems, techniques, and computer-program products that, individually and in combination, permit machine condition monitoring are provided. In some aspects, state estimation and anomaly localization can be determined jointly. To that end, in some embodiments, systems can be configured using at least a synthetic training dataset. The synthetic training dataset includes sensor output data that incorporates synthetic a random amount of noise to each one of multiple sensor devices that probe an industrial machine. The training dataset also includes synthetic information indicative of location of anomalous sensor device(s) of the multiple sensor devices. Therefore, the systems can learn to determine state estimation and anomalous localization concurrently, in a single operation. Accordingly, the training of the systems is consistent with the operation of the systems during machine condition monitoring. Embodiments of the disclosure provide superior predictive performance over conventional machine condition monitoring approaches.
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25.发明申请公开(公告)号：US20210133018A1
公开(公告)日：2021-05-06
申请号：US16477253
申请日：2018-01-22
Applicant: Siemens Aktiengesellschaft
Inventor： Amit Chakraborty , Chao Yuan
Abstract: A computer-implemented method for performing machine condition monitoring for fault diagnosis includes collecting multivariate time series data from a plurality of sensors in a machine and partitioning the multivariate time series data into a plurality of segment clusters. Each segment cluster corresponds to one of a plurality of class labels related to machine condition monitoring. Next, the segment clusters are clustered into segment cluster prototypes. The segment clusters and the segment cluster prototypes are used to learn a discriminative model that predicts a class label. Then, as new multivariate time series data is collected from the sensors in the machine, the discriminative model may be used to predict a new class label corresponding to segments included in the new multivariate time series data. If the new class label indicates a potential fault in operation of the machine, a notification may be provided to one or more users.
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Patent Agency Ranking
公开(公告)号：US20210089275A1
公开(公告)日：2021-03-25
申请号：US17005631
申请日：2020-08-28
Applicant: Siemens Aktiengesellschaft
Inventor： Biswadip Dey , Yaofeng Zhong , Amit Chakraborty
Abstract: System and method for synthesizing a controller for a dynamical system includes a feeder neural network trained to estimate an ordinary differential equation (ODE) from time series training data (X) of a trajectory having embedded angular data and configured to learn dynamics of a physical system by encoding a generalization of a Hamiltonian representation of the dynamics using a constant external control term (u). A neural ODE solver receives the estimate of the ODE from the feeder neural network and synthesizes a controller to control the system to track a reference configuration.
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公开(公告)号：US20200080796A1
公开(公告)日：2020-03-12
申请号：US16516467
申请日：2019-07-19
Applicant: Siemens Aktiengesellschaft
Inventor： Arindam Dasgupta , Amit Chakraborty , Anand A. Kulkarni
Abstract: 3D printed thermal management devices and corresponding methods of manufacturing are described herein. A thermal management device includes a single contiguous component. The single contiguous component includes a body, a plurality of first fins extending away from the body and a plurality of second fins extending away from the body. A surface area of each first fin of the plurality of first fins is different than a surface area of each second fin of the plurality of second fins. A fin density of the plurality of first fins is different than a fin density of the plurality of second fins.
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公开(公告)号：US10540422B2
公开(公告)日：2020-01-21
申请号：US15301481
申请日：2014-04-14
Applicant: Siemens Aktiengesellschaft
Inventor： Chao Yuan , Amit Chakraborty , Eberhard Ritzhaupt-Kleissl , Holger Hackstein
Abstract: A method of predicting an amount of power that will be generated by a solar power plant at a future time includes: forecasting a value of a data variable at the future time that is likely to affect the ability of the solar power plant to produce electricity (S301); computing a plurality of features from prior observed amounts of power generated by the power plant during different previous durations (S302); determining a trending model from the computed features and the forecasted value (S303); and predicting the amount of power that will be generated by the power plant at the future time from the determined model (S304).
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公开(公告)号：US20190188581A1
公开(公告)日：2019-06-20
申请号：US15844728
申请日：2017-12-18
Applicant: Siemens Aktiengesellschaft
Inventor： Guillaume Chabin , Ioannis Akrotirianakis , Amit Chakraborty
Abstract: A computer-implemented method for performing predictive maintenance includes executing a fleet prediction process. During this fleet prediction process, a plurality of fleet data records is collected. Each fleet data record comprises sensor data from a particular physical component in a fleet of physical components. A plurality of component maintenance predictions related to the fleet of physical components is generated. Each component maintenance prediction corresponds to a particular physical component. The plurality of component predictions are merged into one or more fleet maintenance predictions and the fleet maintenance predictions are presented to one or more users. Following the fleet prediction process, a next execution of the fleet prediction process is scheduled based on the fleet maintenance predictions.
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公开(公告)号：US20190034802A1
公开(公告)日：2019-01-31
申请号：US15662917
申请日：2017-07-28
Applicant: Siemens Aktiengesellschaft
Inventor： Prashanth Harshangi , Ioannis Akrotirianakis , Amit Chakraborty
Abstract: The present embodiments relate to reducing the input dimensions to a machine-based Bayesian Optimization using stacked autoencoders. By way of introduction, the present embodiments described below include apparatuses and methods for pre-processing a digital input to a machine-based Bayesian Optimization to a lower the dimensional space of the input, thereby lowering the bounds of the Bayesian optimization. The output of the Bayesian Optimization is then projected back into the original dimensional space to determine input and output values in the original dimensional apace. As such, the optimization is performed by the machine in a lower dimension using the stacked autoencoder to constrain the input dimensions to the optimization.
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