Fault diagnosis method for underground coal mining equipment audio signals based on improved transfer learning

Fault diagnosis method for underground coal mining equipment audio signals based on improved transfer learning

The underground production environment in coal mines is harsh, and key equipment such as gas pumps, ventilators, and coal shearers often operate continuously, making them susceptible to faults. Currently, end-to-end audio data fault diagnosis methods heavily depend on data labeling for model trainin...

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Bibliographic Details
Main Authors: QIU Ji'er, WANG Qi, WANG Peng
Format: Article
Language:zho
Published: Editorial Department of Industry and Mine Automation 2025-02-01
Series:Gong-kuang zidonghua
Subjects:
underground coal mining equipment
audio signal
fault diagnosis
transfer learning
mel-frequency cepstral coefficients
mfcc
maximum mean discrepancy
multi-kernel joint maximum mean discrepancy
source domain
target domain
Online Access:http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025010027
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http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025010027

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