公开(公告)号：US11500370B2

公开(公告)日：2022-11-15

申请号：US16547374

申请日：2019-08-21

Applicant: Hitachi, Ltd.

Inventor： Shuai Zheng ,  Ahmed Khairy Farahat ,  Chetan Gupta

IPC: G05B23/02 ,  G06K9/62 ,  G06N3/08

Abstract: Example implementations involve a system for Predictive Maintenance using Generative Adversarial Networks for Failure Prediction. Through utilizing three processes concurrently and training them iteratively with data-label pairs, example implementations described herein can thereby generate a more accurate predictive maintenance model than that of the related art. Example implementations further involve shared networks so that the three processes can be trained concurrently while sharing parameters with each other.

公开(公告)号：US11693392B2

公开(公告)日：2023-07-04

申请号：US16262778

申请日：2019-01-30

Applicant: Hitachi, Ltd.

Inventor： Shuai Zheng ,  Chetan Gupta ,  Susumu Serita

IPC: G06N3/04 ,  G06N3/08 ,  G05B19/418

CPC classification number: G05B19/41865 ,  G06N3/04 ,  G06N3/08 ,  G05B2219/32335 ,  G05B2219/34379 ,  G05B2219/40499

Abstract: Example implementations described herein are directed to a system for manufacturing dispatching using reinforcement learning and transfer learning. The systems and methods described herein can be deployed in factories for manufacturing dispatching for reducing job-due related costs. In particular, example implementations described herein can be used to reduce massive data collection and reduce model training time, which can eventually improve dispatching efficiency and reduce factory cost.

公开(公告)号：US20230206111A1

公开(公告)日：2023-06-29

申请号：US17561397

申请日：2021-12-23

Applicant: Hitachi, Ltd.

Inventor： Mahbubul ALAM ,  Dipanjan GHOSH ,  Ahmed FARAHAT ,  Laleh JALALI ,  Chetan GUPTA ,  Shuai Zheng

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Example implementations described herein can involve systems and methods involving, for receipt of input data from one or more assets, identifying and separating different event contexts from the input data; training a plurality of machine learning models for each of the different event contexts; selecting a best performing model from the plurality of machine learning models to form a compound model; selecting a best performing subset of the input data for the compound model based on maximizing a metric; and deploying the compound model for the selected subset.

公开(公告)号：US11288577B2

公开(公告)日：2022-03-29

申请号：US16074495

申请日：2016-10-11

Applicant: Hitachi, Ltd.

Inventor： Shuai Zheng ,  Kosta Ristovski ,  Chetan Gupta ,  Ahmed Farahat

IPC: G06N3/08 ,  G06N3/04 ,  G06F11/00 ,  G06F11/30

Abstract: Example implementations described herein are directed to systems and methods for estimating the remaining useful life of a component or equipment through the application of models for deriving functions that can express the remaining useful life over time. In an aspect, the failure acceleration time point is determined for a given type of component, and a function is derived based on the application of models on the failure acceleration time point.