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Embedding Space Reconstruction to Enhance ANN for Industrial Process Fault Diagnosis

Published: 01 June 2024 Publication History

Abstract

How to extract effective fault features from high-dimensional space to assist fault diagnosis has become a huge challenge and research hotspot. With the development of hardware technology, deeper neural network structures can be widely applied, so deep learning methods are increasingly used in the field of fault diagnosis. However, the phenomenon of vanishing gradient is a difficulty in the training of deep learning networks. To eliminate this problem, a novel kernel method is proposed to be applied in the network that is most prone to the problem of vanishing gradient, namely Embedding Space Reconstruction. At the same time, to solve the problem of ignoring the features between classes using Embedding Space Reconstruction, a comparative learning method is proposed to make the features of the same class more aggregated and the features between classes more distant.

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  1. Embedding Space Reconstruction to Enhance ANN for Industrial Process Fault Diagnosis

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    AISNS '23: Proceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security
    December 2023
    467 pages
    ISBN:9798400716966
    DOI:10.1145/3661638
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 01 June 2024

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