Research on hoisting skill level of hydro turbine rotor based on human brain cognitive behavior

The primary purpose of the study is to distinguish the differences in operating levels of hydro turbine rotor hoisting between novice and experienced operators from the viewpoint of human cognitive behavior. This study utilized the characteristics of electroencephalogram (EEG) signals and employed the Multi-scale Residual Shrinkage Network (MS-RSN) method to investigate the differences in operational proficiency among personnel with different levels of operational experience. The research results show that EEG signals can effectively reflect the proficiency of the operator. The MS-RSN method shows high accuracy in identifying the operator's hoisting proficiency, with an accuracy rate of 98.66%.

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Publication Details

Journal
Computer Methods in Biomechanics & Biomedical Engineering
Published
2026-04-21
DOI
https://doi.org/10.1080/10255842.2026.2656798
Primary Topic
Hydraulic and Pneumatic Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on hoisting skill level of hydro turbine rotor based on human brain cognitive behavior

Xiaolei Zhang, Qi Guo, Fuwang Wang
Computer Methods in Biomechanics & Biomedical Engineering
Hydraulic and Pneumatic Systems
article

Research on hoisting skill level of hydro turbine rotor based on human brain cognitive behavior

Xiaolei Zhang, Qi Guo, Fuwang Wang
article en

Abstract

The primary purpose of the study is to distinguish the differences in operating levels of hydro turbine rotor hoisting between novice and experienced operators from the viewpoint of human cognitive behavior. This study utilized the characteristics of electroencephalogram (EEG) signals and employed the Multi-scale Residual Shrinkage Network (MS-RSN) method to investigate the differences in operational proficiency among personnel with different levels of operational experience. The research results show that EEG signals can effectively reflect the proficiency of the operator. The MS-RSN method shows high accuracy in identifying the operator's hoisting proficiency, with an accuracy rate of 98.66%.

Computer Methods in Biomechanics & Biomedical Engineering
Electric Power University (VN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 10%
Hydraulic and Pneumatic Systems
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