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公开(公告)号：US11312430B2
公开(公告)日：2022-04-26
申请号：US16332210
申请日：2017-08-07
Applicant: Hitachi, Ltd.
Inventor： Susumu Serita , Chetan Gupta
Abstract: Example implementations described herein are directed to a system for lean angle estimation without requiring specialized calibration. In example implementations, the mobile device sensor data can be utilized without any additional specialized data or configuration to estimate the lean angle of a motorcycle. The lean angle is determined based on a determination of a base attitude of a mobile device and a measured attitude of the mobile device.
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公开(公告)号：US11231703B2
公开(公告)日：2022-01-25
申请号：US16540810
申请日：2019-08-14
Applicant: Hitachi, Ltd.
Inventor： Chi Zhang , Ahmed Khairy Farahat , Chetan Gupta , Karan Aggarwal
Abstract: Example implementations described herein involve, for data having incomplete labeling to generate a plurality of predictive maintenance models, processing the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for the plurality of predictive maintenance models configured to conduct tasks to determine outcomes for one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and executing the predictive maintenance models on subsequently recorded data.
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公开(公告)号：US11210770B2
公开(公告)日：2021-12-28
申请号：US16355419
申请日：2019-03-15
Applicant: Hitachi, Ltd.
Inventor： Maria Teresa Gonzalez Diaz , Dipanjan Ghosh , Adriano Arantes , Michiko Yoshida , Jiro Hashizume , Chetan Gupta , Phawis Thammasorn
Abstract: Example implementations described herein involve defect analysis for images received from a camera system, which can involve applying a first model configured to determine regions of interest of the object from the images, applying a second model configured to identify localized areas of the object based on the regions of interest on the images; and applying a third model configured to identify defects in the localized ones of the images.
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公开(公告)号：US11120383B2
公开(公告)日：2021-09-14
申请号：US15993344
申请日：2018-05-30
Applicant: Hitachi, Ltd.
Inventor： Susumu Serita , Haiyan Wang , Chetan Gupta
Abstract: A system is provided for operator profiling based on pre-installed sensor measurement. In example implementations, the system extracts a set of segmented time series data associated with a unit of operation and build models which distinguish the operators by machine learning algorithms. The system uses the models to output the evaluation score assigned to each operation, identify the key movements for skilled/non-skilled operators, and recommends appropriate actions to improve operation skill or adjust the scheduling of the operators.
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公开(公告)号：US11037573B2
公开(公告)日：2021-06-15
申请号：US16121837
申请日：2018-09-05
Applicant: HITACHI, LTD.
Inventor： Adriano Siqueira Arantes , Marcos Vieira , Chetan Gupta , Ahmed Khairy Farahat , Maria Teresa Gonzalez Diaz
IPC: G10L15/22 , G10L17/22 , G06N20/00 , G10L13/00 , G10L17/00 , G06Q10/00 , H04N1/00 , G06F16/9535 , G10L25/63
Abstract: In some examples, a system may receive from a device, speech sound patterns corresponding to a voice input related to equipment. Further, the system may determine an identity of a person associated with the device, and may identify the equipment related to the voice input. Using at least one of the received speech sound patterns or a text conversion of the speech sound patterns, along with an equipment history of the identified equipment, as input to one or more machine learning models, the system may determine, at least partially, an instruction related to the equipment. Additionally, the system may send, to the device, the instruction related to the equipment as an audio file for playback on the device.
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Patent Agency Ranking
公开(公告)号：US11693924B2
公开(公告)日：2023-07-04
申请号：US16433858
申请日：2019-06-06
Applicant: Hitachi, Ltd.
Inventor： Hamed Khorasgani , Chetan Gupta , Ahmed Khairy Farahat , Arman Hasanzadehmoghimi
IPC: G06F18/2323 , G06N20/00 , G05B19/406 , H04L67/12
CPC classification number: G06F18/2323 , G05B19/406 , G06N20/00 , G05B2219/32222 , H04L67/12
Abstract: Example implementations involve fault detection and isolation in industrial networks through defining a component as a combination of measurements and parameters and define an industrial network as a set of components connected with different degrees of connections (weights). Faults in industrial network are defined as unpermitted changes in component parameters. Further, the fault detection and isolation in industrial networks are formulated as a node classification problem in graph theory.
Example implementations detect and isolate faults in industrial networks through 1) uploading/learning network structure, 2) detecting component communities in the network, 3) extracting features for each community, 4) using the extracted features for each community to detect and isolate faults, 5) at each time step, based on the faulty components provide maintenance recommendation for the network.-
公开(公告)号：US20230177403A1
公开(公告)日：2023-06-08
申请号：US17542266
申请日：2021-12-03
Applicant: Hitachi, Ltd.
Inventor： Hsiu-Khuern TANG , Haiyan Wang , Chetan Gupta
CPC classification number: G06N20/20 , G06K9/6228 , G06K9/6256 , G06K9/6298
Abstract: Example implementations described herein are directed to systems and methods for predicting if a conjunction of multiple events will occur within a certain time. It relies on an approximate decomposition into subproblems and a search among the possible decompositions and hyperparameters for the best model. When the conjunction is rare, the method mitigates the problem of data imbalance by estimating events that are less rare.
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公开(公告)号：US11544676B2
公开(公告)日：2023-01-03
申请号：US16729657
申请日：2019-12-30
Applicant: HITACHI, LTD.
Inventor： Dipanjan Ghosh , Ahmed Khairy Farahat , Chi Zhang , Marcos Vieira , Chetan Gupta
Abstract: In some examples, a computer system may receive historical repair data and may extract features from the historical repair data for use as training data. The computer system may determine, from the historical repair data, a repair hierarchy including a plurality of repair levels which includes repair actions as one of the repair levels. Furthermore, the computer system may train the machine learning model, which performs multiple tasks for predicting values of individual levels of the repair hierarchy, by tuning parameters of the machine learning model using the training data.
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公开(公告)号：US11544134B2
公开(公告)日：2023-01-03
申请号：US16990662
申请日：2020-08-11
Applicant: Hitachi, Ltd.
Inventor： Hamed Khorasgani , Ahmed Khairy Farahat , Chetan Gupta , Wei Huang
Abstract: Example implementations described herein involve a new data-driven analytical redundancy relationship (ARR) generation for fault detection and isolation. The proposed solution uses historical data during normal operation to extract the data-driven ARRs among sensor measurements, and then uses them for fault detection and isolation. The proposed solution thereby does not need to rely on the system model, can detect and isolate more faults than traditional data-driven methods, can work when the system is not fully observable, and does not rely on a vast amount of historical fault data, which can save on memory storage or database storage. The proposed solution can thereby be practical in many real cases where there are data limitations.
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公开(公告)号：US11215535B2
公开(公告)日：2022-01-04
申请号：US16684269
申请日：2019-11-14
Applicant: Hitachi, Ltd.
Inventor： Wei Huang , Chetan Gupta , Ahmed Khairy Farahat
Abstract: Example implementations described herein involve systems and methods for conducting feature extraction on a plurality of templates associated with vibration sensor data for a moving equipment configured to conduct a plurality of tasks, to generate a predictive maintenance model for the plurality of tasks, the predictive maintenance model configured to provide one or more of fault detection, failure prediction, and remaining useful life (RUL) estimation.
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