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    EQUIPMENT REPAIR MANAGEMENT AND EXECUTION
    21.
    发明申请
    EQUIPMENT REPAIR MANAGEMENT AND EXECUTION 审中-公开

    公开(公告)号：US20200097921A1

    公开(公告)日：2020-03-26

    申请号：US16139149

    申请日：2018-09-24

    Applicant: HITACHI, LTD.

    Inventor： Dipanjan GHOSH ,  Ahmed Khairy FARAHAT ,  Chi ZHANG ,  Marcos VIEIRA ,  Chetan GUPTA

    IPC: G06Q10/00 ,  G06N3/08

    Abstract: In some examples, a computer system may receive historical repair data for first equipment, and may extract features from the historical repair data for the first equipment as training data including one or more of: free-text variables associated with comments related to the first equipment; usage attributes associated with the first equipment; equipment attributes associated with the first equipment; sensor data associated with the first equipment; or event data associated with the first equipment. The system may determine a repair hierarchy including a plurality of repair levels for the equipment. The system may use the training data to train a machine learning model as a multilayer model trained to perform multiple tasks for predicting individual levels of the repair hierarchy. The system may receive a repair request associated with second equipment and uses the machine learning model to determine at least one repair action based on the received repair request.

    A SYSTEM FOR MAINTENANCE RECOMMENDATION BASED ON FAILURE PREDICTION
    22.
    发明申请
    A SYSTEM FOR MAINTENANCE RECOMMENDATION BASED ON FAILURE PREDICTION 审中-公开

    公开(公告)号：US20200057689A1

    公开(公告)日：2020-02-20

    申请号：US16324867

    申请日：2017-07-26

    Applicant: Hitachi, Ltd.

    Inventor： Ahmed Khairy FARAHAT ,  Chetan GUPTA ,  Kosta RISTOVSKI

    IPC: G06F11/07 ,  G06Q30/02 ,  G06N5/02

    Abstract: Example implementations described herein involve a system for maintenance recommendation based on data-driven failure prediction. The example implementations can involve estimating the probability of having a failure event in the near future given sensor measurements and events from the equipment, and then alerts the system user or maintenance staff if the probability of failure exceeds a certain threshold. The example implementations utilize historical failure cases along with the associated sensor measurements and events to learn a group of classification models that differentiate between failure and non-failure cases. In example implementations, the system then chooses the optimal model for failure prediction such that the overall cost of the maintenance process is minimized.

    DEFECT RATE ANALYTICS TO REDUCE DEFECTIVENESS IN MANUFACTURING
    23.
    发明申请
    DEFECT RATE ANALYTICS TO REDUCE DEFECTIVENESS IN MANUFACTURING 审中-公开

    公开(公告)号：US20200004219A1

    公开(公告)日：2020-01-02

    申请号：US16025865

    申请日：2018-07-02

    Applicant: Hitachi, Ltd.

    Inventor： Qiyao WANG ,  Susumu SERITA ,  Chetan GUPTA

    IPC: G05B19/406

    Abstract: Example implementations described herein are directed to systems and methods for defect rate analytics to reduce defectiveness in manufacturing. In an example implementation, a method include determining, from data associated with each feature for a manufacturing process, the data feature indicative of process defects detected based on the feature, an estimated condition for the feature that reduces a defect rate of the process defects, the estimated condition indicating the data into a first group and second group; calculating the rate reduction of the defect rate based on a difference in defects between the first group and the second group; for the rate reduction meeting a target confidence level for a target defect rate, applying the estimated condition to the manufacturing process associated with each of the features. In example implementations, the defect rate analytics reduce defectiveness in manufacturing with independent processes and/or dependent processes.

    EQUIPMENT CONTROL BASED ON FAILURE DETERMINATION
    26.
    发明申请
    EQUIPMENT CONTROL BASED ON FAILURE DETERMINATION 审中-公开

    公开(公告)号：US20180017467A1

    公开(公告)日：2018-01-18

    申请号：US15208662

    申请日：2016-07-13

    Applicant: Hitachi, Ltd.

    Inventor： Tomoaki HIRUTA ,  Chetan GUPTA

    IPC: G01M99/00 ,  G06F17/18 ,  G05B17/02

    CPC classification number: G01M99/008 ,  G05B17/02 ,  G05B23/0221 ,  G05B23/0254 ,  G06F17/18

    Abstract: In some examples, a computing device may generate simulated data based on a physical model of equipment. For example, the simulated data may include a plurality of probability distributions of a plurality of degrees of failure, respectively, for at least one failure mode of the equipment. In addition, the computing device may receive sensor data indicating a measured metric of the equipment. The computing device may compare the received sensor data with the simulated data to determine a failure mode and a degree of failure of the equipment. At least partially based on the determined failure mode and degree of failure of the equipment, the computing device may send at least one of a notification or a control signal.

    OIL AND GAS RIG DATA AGGREGATION AND MODELING SYSTEM
    27.
    发明申请
    OIL AND GAS RIG DATA AGGREGATION AND MODELING SYSTEM 有权
    油气数据聚集与建模系统

    公开(公告)号：US20150278407A1

    公开(公告)日：2015-10-01

    申请号：US14473394

    申请日：2014-08-29

    Applicant: HITACHI, LTD.

    Inventor： Ravigopal VENNELAKANTI ,  Umeshwar DAYAL ,  Chetan GUPTA

    IPC: G06F17/50 ,  G06N99/00 ,  G06N7/00

    CPC classification number: E21B43/00 ,  E21B44/00 ,  E21B49/003

    Abstract: A management server is coupled to a plurality of rig systems by a network, each of the rig systems having a plurality of sensors, a rig and a rig node. The management server stores data received from at least one of the plurality of rig systems, the data including values associated with one or more attributes of the rig. The management server derives a model signature for at least one phase from a timeline for at least one rig system based on analytics of information stored in the database and the data, where the model signature includes a set of attributes for the at least one of the plurality of rig systems. In addition, the management server generates a recommendation including one or more actions for planning rig system management operations corresponding to at least one attribute of the set of attributes.

    Abstract translation: 管理服务器通过网络耦合到多个钻机系统，每个钻机系统具有多个传感器，钻机和钻机节点。 管理服务器存储从多个钻机系统中的至少一个接收的数据，该数据包括与钻机的一个或多个属性相关联的值。 管理服务器基于对存储在数据库中的信息的分析和数据，为至少一个钻机系统的时间线中的至少一个阶段导出模型签名，其中模型签名包括用于至少一个钻机系统的至少一个的属性集合 多台钻机系统。 此外，管理服务器生成包括用于规划钻机系统管理操作的一个或多个动作的建议，该操作对应于所述一组属性的至少一个属性。

    VIBRATION DATA ANALYSIS WITH FUNCTIONAL NEURAL NETWORK FOR PREDICTIVE MAINTENANCE
    28.
    发明公开
    VIBRATION DATA ANALYSIS WITH FUNCTIONAL NEURAL NETWORK FOR PREDICTIVE MAINTENANCE 审中-公开

    公开(公告)号：US20230222322A1

    公开(公告)日：2023-07-13

    申请号：US17574208

    申请日：2022-01-12

    Applicant: Hitachi, Ltd.

    Inventor： Wei HUANG ,  Haiyan WANG ,  Qiyao WANG ,  Ahmed FARAHAT ,  Chetan GUPTA

    IPC: G06N3/04

    CPC classification number: G06N3/0454

    Abstract: An apparatus for predicting a characteristic of a system is provided. The apparatus may include a memory and at least one processor coupled to the memory. The at least one processor may be configured to perform a method including measuring, at a high sample rate, data relating to an operation of the system over a first time period. The method may further include producing a two-dimensional (2D) time-and-frequency input data set by applying a wavelet transform to the measured data. The method may additionally include generating a set of one or more values associated with one or more system characteristics by processing the 2D time-and-frequency input data set using a functional neural network (FNN).

    SYSTEM FOR PREDICTIVE MAINTENANCE USING DISCRIMINANT GENERATIVE ADVERSARIAL NETWORKS
    30.
    发明申请
    SYSTEM FOR PREDICTIVE MAINTENANCE USING DISCRIMINANT GENERATIVE ADVERSARIAL NETWORKS 有权

    公开(公告)号：US20210279597A1

    公开(公告)日：2021-09-09

    申请号：US17066199

    申请日：2020-10-08

    Applicant: Hitachi, Ltd.

    Inventor： Shuai ZHENG ,  Chetan GUPTA

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Example implementations described herein involve a system for Predictive Maintenance using Discriminant Generative Adversarial Networks, and can involve providing generated sensor data and real sensor data to a first network and to a second network, the first network configured to enforce a discriminant loss objective of the second network, the second network configured to distinguish between the generated sensor data and the real sensor data, the first network including a subset of layers from the second network, the real sensor data including pairs of real sensor data and labels, the second network integrated into a generative adversarial network (GAN); training the machine health classification model from the output of the first network using the provided generated sensor data and the real sensor data, the output of the first network including feature vectors; and deploying the machine health classification model with the first network.

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