LSTM Network Structure.

<div><p>Monitoring the status of circular saw blades is an effective measure to ensure the production efficiency and safety of spent fuel assembly cutting. However, the prediction of wear during the cutting of stainless steel shells of spent fuel assemblies by circular saw blades is not...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Chao Zeng (452370) (author)
مؤلفون آخرون:	Chengchao Wang (13851254) (author), Xueqin Xiong (21567940) (author), Xiangjiang Wang (6276875) (author), Sheng Xiao (592094) (author)
منشور في:	2025
الموضوعات:	Medicine Biotechnology Sociology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> monitoring study effectively solves stainless steel shells spent fuel assemblies small sample sizes pearson correlation coefficient important practical significance generative adversarial network dual feature fusion complicated working conditions complex working conditions circular saw blades 100 %, higher main fault types lstm model based total data volume data volume fault state data acquisition vibration signals utilized aiming term memory sampling overlapping neural networks long short insufficient robustness forecasting consequence entirely accurate effective measure accurate method accuracy rate 9 %). 300 samples 2100 samples 2 %)
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LSTM Network Structure.

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