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公开(公告)号：US20210248444A1
公开(公告)日：2021-08-12
申请号：US16784566
申请日：2020-02-07
Applicant: Hitachi, Ltd.
Inventor： Qiyao WANG , Haiyan WANG , Chetan GUPTA , Hamed KHORASGANI , Huijuan SHAO , Aniruddha Rajendra RAO
Abstract: In example implementations described herein, there are systems and methods for processing sensor data from an equipment over a period of time to generate sensor time series data; processing the sensor time series data in a kernel weight layer configured to generate weights to weigh the sensor time series data; providing the weighted sensor time series data to fully connected layers configured to conduct a correlation on the weighted sensor time series data with predictive maintenance labels to generate an intermediate predictive maintenance label; and providing the intermediate predictive maintenance label to an inversed kernel weight layer configured to inverse the weights generated by the kernel weight layer, to generate a predictive maintenance label for the equipment.
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公开(公告)号：US20210148791A1
公开(公告)日：2021-05-20
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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公开(公告)号：US20200380447A1
公开(公告)日：2020-12-03
申请号：US16428141
申请日：2019-05-31
Applicant: Hitachi, Ltd.
Inventor： Qiyao WANG , Haiyan WANG , Susumu SERITA , Takashi SAEKI , Chetan GUPTA
Abstract: Example implementations described herein involve systems and methods involving a plurality of sensors monitoring one or more processes, the sensors providing sensor data, which can include determining a probability map of the sensor data from a database and a functional relationship between key performance indicators (KPIs) of the one or more processes and the sensor data; executing a search on the probability map to determine constrained and continuous ranges for the sensor data that optimize KPIs for the one or more processes based on the functional relationship; and generating a recommendation for the one or more processes that fit within the constrained and continuous range of the sensor data.
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公开(公告)号：US20200380388A1
公开(公告)日：2020-12-03
申请号：US16428016
申请日：2019-05-31
Applicant: Hitachi, Ltd.
Inventor： Qiyao WANG , Shuai ZHENG , Ahmed FARAHAT , Susumu SERITA , Takashi SAEKI , Chetan GUPTA
Abstract: Example implementations described herein are directed to constructing prediction models and conducting predictive maintenance for systems that provide sparse sensor data. Even if only sparse measurements of sensor data are available, example implementations utilize the inference of statistics with functional deep networks to model prediction for the systems, which provides better accuracy and failure prediction even if only sparse measurements are available.
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公开(公告)号：US20200258057A1
公开(公告)日：2020-08-13
申请号：US16332101
申请日：2017-10-06
Applicant: HITACHI, LTD.
Inventor： Ahmed Khairy FARAHAT , Chetan GUPTA , Marcos VIEIRA , Susumu SERITA
Abstract: In some examples, a computer system may receive historical repair data for equipment and/or domain knowledge related to the equipment. The system may construct a hierarchical data structure for the equipment including a first hierarchy and a second hierarchy, the first hierarchy including a plurality of equipment nodes corresponding to different equipment types, and the second hierarchy including a plurality of repair category nodes corresponding to different repair categories. The system may generate a plurality of machine learning models corresponding to the plurality of repair category nodes, respectively. When the system receives a repair request associated with the equipment, the system determines a certain one of the equipment nodes associated with the equipment, and based on determining that a certain repair category node is associated with the certain equipment node, uses the machine learning model associated with the certain repair category node to determine one or more repair actions.
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Patent Agency Ranking
公开(公告)号：US20190235484A1
公开(公告)日：2019-08-01
申请号：US15884986
申请日：2018-01-31
Applicant: Hitachi, Ltd.
Inventor： Kosta RISTOVSKI , Chetan GUPTA , Ahmed FARAHAT , Onur ATAN
CPC classification number: G05B23/0283 , G06F17/11 , G06N5/04 , G06N7/005 , G06N20/00
Abstract: Example implementations described herein involve a system for maintenance predictions generated using a single deep learning architecture. The example implementations can involve managing a single deep learning architecture for three modes including a failure prediction mode, a remaining useful life (RUL) mode, and a unified mode. Each mode is associated with an objective function and a transformation function. The single deep learning architecture is applied to learn parameters for an objective function through execution of a transformation function associated with a selected mode using historical data. The learned parameters of the single deep learning architecture can be applied with streaming data from with the equipment to generate a maintenance prediction for the equipment.
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37.发明申请公开(公告)号：US20190104028A1
公开(公告)日：2019-04-04
申请号：US15536914
申请日：2015-01-30
Applicant: HITACHI, LTD.
Inventor： Anshuman SAHU , Chetan GUPTA , Song WANG , Umeshwar DAYAL
CPC classification number: H04L41/147 , G06F11/008 , G06F11/3006 , G06F11/3419 , G06F11/3442 , G06F11/3447 , G06F11/3452 , G06F2201/875 , H04L41/142 , H04L41/145 , H04L41/5019 , H04L43/0876 , H04L67/1097
Abstract: Example implementations disclosed herein are directed to monitoring communications networks in real time to ensure that they are performing in a desired fashion. Metrics such as utilization and latency are monitored to ensure that customer SLAs (Service Level Agreements) are met in a timely fashion. To detect problems in advance, example implementations predict the future values of metrics and detect events if the future value violates certain conditions. Example implementations build models that can predict the future values of metrics and analyze historical, near real time and real time streaming data so as to build predictive models.
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公开(公告)号：US20240386279A1
公开(公告)日：2024-11-21
申请号：US18199498
申请日：2023-05-19
Applicant: HITACHI, Ltd.
Inventor： Aniruddha Rajendra RAO , Jana Cathrin BACKHUS , Ahmed FARAHAT , Chetan GUPTA
IPC: G06N3/096
Abstract: Systems and methods described herein can involve learning a functional neural network (FNN) for a source domain associated with source time series data, the learning involving learning functional parameters of the FNN, the FNN comprising a plurality of layers of continuous neurons; transferring the functional parameters of the FNN to a target domain that is separate from the source domain; and tuning the functional parameters of the FNN with target time series data from the target domain, the target time series data having fewer samples than the source time series data.
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公开(公告)号：US20240249112A1
公开(公告)日：2024-07-25
申请号：US18100455
申请日：2023-01-23
Applicant: Hitachi, Ltd.
Inventor： Dipanjan GHOSH , Ahmed FARAHAT , Chetan GUPTA
Abstract: Example implementations described herein are directed to systems and methods for generating a model ensemble to reduce bias, the method involving training a plurality of machine learning models from data, each of the plurality of machine learning models trained from a first subset of the data and validated from a second subset of the data, each of the first subset and the second subset being different for each of the plurality of machine learning models; determining accuracy of each of the plurality of machine learning models based on validation against the second subset of the data; pruning the plurality of machine learning models based on the accuracy to generate a subset of the plurality of machine learning models; and forming the model ensemble from the subset of the plurality of machine learning models.
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公开(公告)号：US20240248438A1
公开(公告)日：2024-07-25
申请号：US18101518
申请日：2023-01-25
Applicant: Hitachi, Ltd.
Inventor： Atsuki KIUCHI , Haiyan WANG , Chetan GUPTA
IPC: G05B13/04 , G06Q10/0639
CPC classification number: G05B13/042 , G06Q10/06393
Abstract: Conventional simulation-based optimization, even when automated, requires substantial user-involved development time. Accordingly, embodiments are disclosed to automate various aspects of simulation-based optimization. In particular, an optimization configurator and associated data structures are disclosed for generating and running optimization templates that can be easily constructed (e.g., via lists of available components), revised, evaluated, and re-run as needed. The optimization templates may comprise a plurality of optimization configurations that each define and pair an optimization algorithm with a simulator of a real-world system. Embodiments can reduce development time, are applicable to various domains, can be used by novice users without specialized knowledge, and can improve the overall quality of optimization for the operations of real-world systems, such as supply chains.
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