公开(公告)号：US20200327886A1

公开(公告)日：2020-10-15

申请号：US16380343

申请日：2019-04-10

Applicant: Hitachi, Ltd.

Inventor： Walid SHALABY ,  Chetan GUPTA ,  Maria Teresa GONZALEZ DIAZ ,  Adriano ARANTES

IPC: G10L15/22 ,  G10L15/18

Abstract: Example implementations involve a framework for knowledge base construction of components and problems in short texts. The framework extracts domain-specific components and problems from textual corpora such as service manuals, repair records, and public Q/A forums using: 1) domain-specific syntactic rules leveraging part of speech tagging (POS), and 2) a neural attention-based seq2seq model which tags raw sentences end-to-end identifying components and their associated problems. Once acquired, this knowledge can be leveraged to accelerate the development and deployment of intelligent conversational assistants for various industrial AI scenarios (e.g., repair recommendation, operations, and so on) through better understanding of user utterances. The example implementations give better tagging accuracy on various datasets outperforming well known off-the-shelf systems.

公开(公告)号：US20220187819A1

公开(公告)日：2022-06-16

申请号：US17118081

申请日：2020-12-10

Applicant: Hitachi, Ltd.

Inventor： Walid SHALABY ,  Mahbubul ALAM ,  Dipanjan GHOSH ,  Ahmed FARAHAT ,  Chetan GUPTA

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

Abstract: Example implementations involve systems and methods for predicting failures and remaining useful life (RUL) for equipment, which can involve, for data received from the equipment comprising fault events, conducting feature extraction on the data to generate sequences of event features based on the fault events; applying deep learning modeling to the sequences of event features to generate a model configured to predict the failures and the RUL for the equipment based on event features extracted from data of the equipment; and executing optimization on the model.