Metallurgical productions fault detection method based on RESLSTM-CNN model

[摘要] 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 %.

[学科分类] 核物理和高能物理

[关键词] metallurgical production;fault;detection;bearing;method network model