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公开(公告)号：US20230325678A1
公开(公告)日：2023-10-12
申请号：US18041002
申请日：2020-08-24
Applicant: Siemens Aktiengesellschaft
Inventor： Dmitriy Fradkin , Marco Gario , Biswadip Dey , Ioannis Akrotirianakis , Georgi Markov , Aditi Roy , Amit Chakraborty
IPC: G06N3/094
CPC classification number: G06N3/094
Abstract: System and method for robust machine learning (ML) includes an attack detector comprising one or more deep neural networks trained using adversarial examples generated from a generative adversarial network (GAN), producing an alertness score based on a likelihood of an input being adversarial. A dynamic ensemble of individually robust ML models of various types and sizes and all being trained to perform an ML-based prediction is dynamically adapted by types and sizes of ML models to be deployed during the inference stage of operation. The adaptive ensemble is responsive to the alertness score received from the attack detector. A data protector module with interpretable neural network models is configured to prescreen training data for the ensemble to detect potential data poisoning or backdoor triggers in initial training data.
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公开(公告)号：US11639711B2
公开(公告)日：2023-05-02
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：US20230064332A1
公开(公告)日：2023-03-02
申请号：US17462648
申请日：2021-08-31
Applicant: Siemens Aktiengesellschaft
Inventor： Ioannis Akrotirianakis , Biswadip Dey , Amit Chakraborty
Abstract: System and method are disclosed for approximating unknown safety constraints during reinforcement learning of an autonomous agent. A controller for directing the autonomous agent includes a reinforcement learning (RL) algorithm configured to define a policy for behavior of the autonomous agent, and a control barrier function (CBF) algorithm configured to calculate a corrected policy that relocates policy states to an edge of a safety region. Iterations of the RL algorithm safely learn an optimal policy where exploration remains within the safety region. CBF algorithm uses standard least squares to derive estimates of coefficients for linear constraints of the safe region. This overcomes inaccurate estimation of safety region constraints caused by one or more noisy observations of constraints received by sensors.
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公开(公告)号：US20210312284A1
公开(公告)日：2021-10-07
申请号：US17261040
申请日：2018-08-23
Applicant: Siemens Aktiengesellschaft
Inventor： Arindam Dasgupta , Feipeng Zhao , Charles A. Carlson, Jr. , Chao Yuan , Amit Chakraborty
Abstract: A system for verification of the output of a sensor includes an industrial system comprising a plurality of sensors, one of the plurality of sensors being a target sensor, a plurality of machine learning networks, each machine learning network connecting a plurality of driving sensors associated with the target sensor and trained using simulation data. a selected machine learning network from the plurality of machine learning networks having an output representative of the target sensor, the selected machine learning network being trained with real-time data from the industrial plant and a processor for comparing an output of the selected machine learning network to a real output of the target sensor. Based on the comparison, the real sensor output is provided as final output when the values match, and the estimated value is output when the values do not match and the sensor output is flagged as an error.
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5.发明申请公开(公告)号：US20210302954A1
公开(公告)日：2021-09-30
申请号：US17269764
申请日：2018-08-31
Applicant: Siemens Aktiengesellschaft
Inventor： Alexis Motto , Amit Chakraborty
Abstract: A method for increasing a meantime between service visits in an industrial system includes receiving event information from at least one information source, building an event network from the received event information, identifying a sequence of events indicative of a fault, and determining a cost-minimizing resolution to address the fault, wherein the event network is configured to identify a sequence of events that do not occur in direct chronological sequence. A services diagnostic engine may be configured to receive the event information, extract features of each event in the event information, identify a relationship between a first event and a second event and create a logical connection between the first and second event. The cost minimizing recommendation includes a remote operation to reset a component, for example remotely resetting a circuit breaker. The cost minimizing recommendation may be carried out automatically or presented to a user for consideration.
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Patent Agency Ranking
公开(公告)号：US10108513B2
公开(公告)日：2018-10-23
申请号：US15303243
申请日：2014-04-16
Applicant: Siemens Aktiengesellschaft
Inventor： Chao Yuan , Amit Chakraborty , Holger Hackstein , Hans Weber
Abstract: A method for predicting failure modes in a machine includes learning (31) a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, learning (32) a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of the source machine and the target machine using the multivariate Gaussian distribution for the independent sensors, transforming (33) data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine, and transforming (34) data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.
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公开(公告)号：US20170205537A1
公开(公告)日：2017-07-20
申请号：US15320899
申请日：2015-06-29
Applicant: Siemens Aktiengesellschaft
Inventor： Chao Yuan , Amit Chakraborty , Holger Hackstein
Abstract: A method for solar forecasting includes receiving a plurality of solar energy data as a function of time of day at a first time, forecasting (620) from the solar energy data a mode, where the mode is a sunny day, a cloudy day, or an overcast day, and the forecast predicts the mode for a next solar energy datum, receiving (622) the next solar energy datum, updating a probability distribution function (pdf) of the next solar energy datum given the mode, updating a pdf of the mode for the next solar energy datum from the updated pdf of the new solar energy datum given the mode, forecasting (624, 626) a plurality of future unobserved solar energy data from the updated pdf of the mode, where the plurality of future unobserved solar energy data and the plurality of solar energy data have a Gaussian distribution for a given mode determined from training data.
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8.发明申请DISCRIMINATIVE HIDDEN KALMAN FILTERS FOR CLASSIFICATION OF STREAMING SENSOR DATA IN CONDITION MONITORING 审中-公开在条件监测中分类传感器数据的识别隐藏卡尔曼滤波器公开(公告)号：US20150142384A1
公开(公告)日：2015-05-21
申请号：US14406606
申请日：2013-06-11
Applicant: Siemens Aktiengesellschaft
Inventor： Yuan Chao , Amit Chakraborty , Holger Hackstein , Leif Wiebking
CPC classification number: G06F11/3089 , G06F11/008 , 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).
Abstract translation: 一种用于监视系统或过程的状况的方法包括从设置在系统内的多个传感器获取传感器数据（S41和S44）。 所获取的传感器数据被实时流式传输到计算机系统（S42和S44）。 使用计算机系统将歧视性框架应用于流传感器数据（S43和S45）。 辨别框架提供概率值，该概率值表示传感器数据表示系统内的异常的概率。 歧视性框架是卡尔曼滤波器与后勤功能的集成（S41）。
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公开(公告)号：US20130110606A1
公开(公告)日：2013-05-02
申请号：US13651515
申请日：2012-10-15
Applicant: Siemens Aktiengesellschaft
Inventor： Tolga Han Seyhan , Amit Chakraborty
IPC: G06Q30/00
Abstract: A method for reducing an amount of power consumed includes reading loads from meters of customers, predicting a future total load from the read loads, determining whether the future total load exceeds a threshold value, sending a request message including terms to at least one of the customers when the future total load exceeds the threshold value, and adjusting an electric bill of a corresponding one of the customers for a period of time based on whether the customer adhered to the terms.
Abstract translation: 一种用于减少功耗量的方法，包括：读取来自客户的数量的负载，预测来自读取负载的未来总负载，确定未来的总负荷是否超过阈值，向包括至少一个 客户在未来总负载超过阈值时，根据客户是否遵守条款，调整相应客户的电费一段时间。
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10.发明授权公开(公告)号：US11443262B2
公开(公告)日：2022-09-13
申请号：US16755905
申请日：2017-10-20
Applicant: Siemens Aktiengesellschaft
Inventor： Ioannis Akrotirianakis , Amit Chakraborty
IPC: G06Q10/06 , G06Q10/04 , G06Q50/06 , G06Q50/04 , G05B19/418
Abstract: A computer-implemented method of scheduling jobs for an industrial process includes receiving jobs to be executed on machines within a manufacturing facility. A job schedule is generated based on an optimization function that minimizes total energy cost for all the machines during a time horizon based on a summation of energy cost at each time step between a start time and an end time. The energy cost at each time step is a summation of (a) a first energy cost associated with each machine in sleeping mode during the time step, (b) a second energy cost associated with each machine in stand-by mode during the time step, and (c) a third energy cost associated with each machine in processing mode during the time step. The jobs are executed on the machines based on the job schedule.
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