Metallurgical productions fault detection method based on RESLSTM-CNN model

Timely detection of abnormal working conditions and accurate diagnosis of abnormal working conditions are of great research significance to ensure the safe and stable operation of metallurgical production processes and to avoid losses caused by faults. In this paper, it propose a residual long and s...

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Bibliographic Details
Published inMetalurgija Vol. 62; no. 2; pp. 247 - 250
Main Authors Z. J. Chen, J. Zhao, M. A. Liu
Format Journal Article
LanguageEnglish
Published Croatian Metallurgical Society 2023
Subjects
bearing
detection
fault
metallurgical production
method network model
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Summary:Timely detection of abnormal working conditions and accurate diagnosis of abnormal working conditions are of great research significance to ensure the safe and stable operation of metallurgical production processes and to avoid losses caused by faults. In this paper, it propose a residual long and short-term memory network and convolutional neural network (RESLSTM-CNN) model for fault detection in metallurgical production processes bearing fault detection with an accuracy of 98,92 %.
ISSN:0543-5846
1334-2576