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

IPC: G06N5/04 ,  G06N20/00

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.

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

IPC: G06Q10/00 ,  G06N3/04 ,  G06N3/08 ,  G06N5/00 ,  G06N7/00 ,  G06N20/00 ,  G10L15/187

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.

公开(公告)号：US20180075235A1

公开(公告)日：2018-03-15

申请号：US15495213

申请日：2017-04-24

Applicant: Hitachi, Ltd.

Inventor： Yoshiyuki TAJIMA ,  Susumu SERITA ,  Masami YAMASAKI

IPC: G06F21/55

CPC classification number: G06F21/554 ,  G06F2221/034

Abstract: An abnormality detection system is configured to (a) convert, based on a prescribed rule, a time-sequential event included in a log output by a monitoring target system into a symbolized event; (b) learn, based on a normal-time log symbolized in (a), a symbolized event sequence, which appears in a same pattern, as a frequently-appearing pattern; and (c) detect an occurrence or a nonoccurrence of an abnormality, based on whether not the frequently-appearing pattern is occurring in a monitoring-time log symbolized in (a).