公开(公告)号：US11328406B2

公开(公告)日：2022-05-10

申请号：US16810129

申请日：2020-03-05

Applicant: Siemens Aktiengesellschaft

Inventor： Arindam Dasgupta ,  Biswadip Dey ,  Anand A. Kulkarni ,  Amit Chakraborty

IPC: G06T7/00 ,  G06T7/62 ,  G06T5/00 ,  G06N20/00

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.

公开(公告)号：US20210342791A1

公开(公告)日：2021-11-04

申请号：US17280223

申请日：2018-09-28

Applicant: Siemens Aktiengesellschaft

Inventor： Ioannis Akrotirianakis ,  Amit Chakraborty

IPC: G06Q10/00 ,  G06Q10/06

Abstract: Systems, techniques, and computer-program products are provided to generate manufacturing schedules that integrate maintenance strategies. A manufacturing schedule can be generated by solving an optimization problem subject to operational constraints that preserve consistency in the order of the operations to be performed during the manufacture of a product, and further subject to maintenance constraints that enforce a desired maintenance strategy. The optimization problem can be solved by minimizing a makespan of a product subject to the operational and maintenance constraints.

公开(公告)号：US10565080B2

公开(公告)日：2020-02-18

申请号：US14406606

申请日：2013-06-11

Applicant: Siemens Aktiengesellschaft

Inventor： Chao Yuan ,  Amit Chakraborty ,  Holger Hackstein ,  Leif Wiebking

IPC: G06F11/30 ,  G06F17/18

Abstract: A method for monitoring a condition of a system or process includes acquiring sensor data from a plurality of sensors disposed within the system (S41 and S44). The acquired sensor data is streamed in real-time to a computer system (S42 and S44). A discriminative framework is applied to the streaming sensor data using the computer system (S43 and S45). The discriminative framework provides a probability value representing a probability that the sensor data is indicative of an anomaly within the system. The discriminative framework is an integration of a Kalman filter with a logistical function (S41).

公开(公告)号：US20180080853A1

公开(公告)日：2018-03-22

申请号：US15267881

申请日：2016-09-16

Applicant: Siemens Aktiengesellschaft

Inventor： Chao Yuan ,  Amit Chakraborty ,  Akshay Patwal ,  Matthew Evans

IPC: G01M15/14 ,  G06N99/00

CPC classification number: G01M15/14 ,  G06N3/02 ,  G06N7/005

Abstract: A system for predicting time-to-failure of a machine includes one or more processors and a non-transitory, computer-readable storage medium in operable communication with the processors. The computer-readable storage medium contains one or more programming instructions that, when executed, cause the processors to receive or retrieve multivariate time series data observed a plurality of times, and infer a plurality of state variables from the multivariate time series data, each state variable describing an operating condition of the machine at a particular time. The instructions further cause the processor to compute an average life consumption rate by applying a life consumption rate model to the plurality of state variables and time-to-failure for the machine based on the average life consumption rate. The time-to-failure for the machine may then be reported to one or more users.

公开(公告)号：US20170092012A1

公开(公告)日：2017-03-30

申请号：US14870412

申请日：2015-09-30

Applicant: Siemens Aktiengesellschaft

Inventor： Sridharan Palanivelu ,  Amit Chakraborty

IPC: G06T19/20 ,  G06T13/20 ,  G06T15/30 ,  G06F17/22 ,  G06F17/27 ,  G06F17/21 ,  G06T11/00 ,  G06F17/24

CPC classification number: G06T19/20 ,  G06T15/30 ,  G06T2210/32 ,  G06T2219/008 ,  G06T2219/2012

Abstract: A system and method for augmenting an existing u3d file to include additional 3D functions/illustrations accessible by anyone who later accesses the file is based upon adding a “composer” to the system used to generate 3D PDF reports. The composer utilizes a high-level specification based on a new “Additional Data Inclusion” (ADI) file format that defines additional types of 3D information that can be added to the existing u3d file. The various types of additional information may include, for example, additional viewing planes, clipping planes, textures, and the like. With the ability to add this type of information, an individual preparing a report including a 3D object is able to augment the supplied 3D information (i.e., the “existing” u3d file) with particular information that may be relevant to those individuals later reviewing the report.

公开(公告)号：US20170076216A1

公开(公告)日：2017-03-16

申请号：US14849649

申请日：2015-09-10

Applicant: Siemens Aktiengesellschaft

Inventor： Ioannis Akrotirianakis ,  Amit Chakraborty ,  Jie Liu

IPC: G06N7/00 ,  G06F9/48

CPC classification number: G06N7/005

Abstract: A generalized autoregressive integrated moving average (ARIMA) model for use in predictive analytics of time series is based upon creating all possible ARIMA models (by knowing a priori the largest possible values of the p, d and q parameters forming the model), and utilizing the results of at least two different performance measures to ultimately choose the ARIMA(p,d,q) model that is most appropriate for the time series under study. The method of the present invention allows each parameter to range over all possible values, and then evaluates the complete universe of all possible ARIMA models based on these combinations of p, d and q to find the specific p, d and q parameters that yield the “best” (i.e., lowest value) performance measure results. This generalized ARIMA model is particularly useful in predicting future operating hours of power plants and scheduling maintenance events on the gas turbines at these plants.

Abstract translation: 用于时间序列预测分析的广义自回归综合移动平均（ARIMA）模型基于创建所有可能的ARIMA模型（通过先验知道形成模型的p，d和q参数的最大可能值），并利用 结果至少有两种不同的表现措施，最终选择ARIMA（p，d，q）模型，这是最适合所研究的时间序列。 本发明的方法允许每个参数范围在所有可能的值之上，然后基于p，d和q的这些组合来评估所有可能的ARIMA模型的完整范围，以找到产生该参数的特定p，d和q参数 “最佳”（即最低价值）表现测量结果。 这种广义的ARIMA模型在预测发电厂的未来运营时间和在这些工厂的燃气轮机上安排维护事件特别有用。

公开(公告)号：US20160018378A1

公开(公告)日：2016-01-21

申请号：US14772088

申请日：2014-02-13

Applicant: Siemens Aktiengesellschaft

Inventor： Chao Yuan ,  Amit Chakraborty ,  Holger Hackstein ,  Ping Zhang ,  Liang Lan

IPC: G01N33/22 ,  G01N21/359 ,  G01N21/3563

CPC classification number: G01N33/222 ,  G01J3/28 ,  G01N21/3563 ,  G01N21/359 ,  G01N2201/129

Abstract: Properties of coal are determined from samples processed by a near-infrared spectroscopy (NIR) device that generates wavelengths dependent spectra. Target values of the properties are associated with the NIR spectra by a kernel based regression model generated from training data based on an anisotropic kernel function that is extended by defining the kernel parameters as a smooth function over the wavelengths associated with a spectrum. Like the anisotropic case each wavelength related dimension has its own kernel parameter. Adjacent dimensions are restricted to have similar kernel parameters. Measured spectra with a limited number of features are reconstructed by applying a regression model based on training data of spectra having an extended number of features. Training data are pruned based on a regression model by removing outliers.

Abstract translation: 煤的性质由通过产生波长依赖光谱的近红外光谱（NIR）装置处理的样品确定。 属性的目标值通过基于内核的回归模型与基于内核的回归模型相关联，所述回归模型基于通过将核参数定义为与频谱相关联的波长上的平滑函数来扩展的各向异性核函数，从训练数据生成。 像各向异性情况一样，每个波长相关尺寸都有自己的内核参数。 相邻的维度被限制为具有相似的内核参数。 通过应用基于具有扩展数量特征的光谱的训练数据的回归模型来重建具有有限数量特征的测量光谱。 通过消除异常值，通过回归模型修剪培训数据。

公开(公告)号：US20240399568A1

公开(公告)日：2024-12-05

申请号：US18325537

申请日：2023-05-30

Applicant: Siemens Aktiengesellschaft

Inventor： Yingnan Cui ,  Yaofeng Zhong ,  Georgia Olympia Brikis ,  Amit Chakraborty ,  Biswadip Dey

IPC: B25J9/16

Abstract: A computer-implemented system and method for synthesizing a controller for an under actuated robotic manipulator includes a machine learning based model having a plurality of neural network modules. Each module is configured to approximate a function related to an underactuated controller for a robotic manipulator. Parameters of each function are learned during training of the model using a loss function that satisfies one or more conditions including structure preservation, integrability and equilibrium assignment.

公开(公告)号：US12033089B2

公开(公告)日：2024-07-09

申请号：US16324663

申请日：2017-09-14

Applicant: Siemens Aktiengesellschaft

Inventor： Yuan Chao ,  Amit Chakraborty

IPC: G06N7/01 ,  G06F17/15 ,  G06N20/00

CPC classification number: G06N7/01 ,  G06F17/15 ,  G06N20/00

Abstract: Systems, methods, and computer-readable media are disclosed for generating and training a deep convolutional generative model for multivariate time series modeling and utilizing the model to assess time series data indicative of a machine or machine component's operational state over a period of time to detect and localize potential operational anomalies.

公开(公告)号：US11922134B2

公开(公告)日：2024-03-05

申请号：US17005631

申请日：2020-08-28

Applicant: Siemens Aktiengesellschaft

Inventor： Biswadip Dey ,  Yaofeng Zhong ,  Amit Chakraborty

IPC: G06N3/08 ,  G06F7/64 ,  G06N3/04 ,  G06N3/045

CPC classification number: G06F7/64 ,  G06N3/045 ,  G06N3/08

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.