Citation

Zhao D, Shen Z, Liu X. China Saf. Sci. J. 2022; 32(10): 76-82.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.10.1766

PMID

unavailable

Abstract

In order to address the difficulty in obtaining failure samples of mine ventilation system and the lack of research on online detection to fill the blank in fault branch diagnosis based on real⁃time monitoring data of sensors, OC⁃SVM and IL method were combined to construct OCISVM model. Firstly, monitoring data of normal samples were used to construct classification hyperplane at offline phase. Then, at online detection stage, classification hyperplane was updated by introducing delta function according to incremental learning, and online fault branch diagnosis was achieved based on threshold criterion. Finally, the proposed model was applied to Dongshan coal mine ventilation system. The results show that the model' s fault diagnosis accuracy can reach as high as 96. 5% while running in milliseconds. Moreover, it demonstrates higher stability when dealing with unbalanced data. © 2022 China Safety Science Journal. All rights reserved.

Language: zh