One-class classification model for intelligent fault diagnosis of mine ventilation systems

• Published in: Scientific reports 2024-11, Vol.14 (1), p.27009-11, Article 27009
• Main Authors: Luo, Wen, Zhao, Youxin
• Format: Article
• Language: English
• Subjects: 639/166; 639/166/986; 639/705/1042; Algorithms; Artificial intelligence; Classification; Coal mines; Coal mining; Experiments; Fault diagnosis; Humanities and Social Sciences; Intelligent algorithm; Machine learning; Mine ventilation system; multidisciplinary; Neural networks; One-class classification integration; One-class support vector machine; Performance evaluation; Science; Science (multidisciplinary); Support vector machines; Ventilation; Wind speed
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-73527-0

To address the problem of fault branch recognition in mine ventilation systems, a one-class classification algorithm is introduced to construct the MC-OCSVM ventilation system fault diagnosis model, which is integrated with multiple OCSVMs. This model adopts uniform hyperparameters and transforms the ventilation system fault diagnosis problem into a maximum decision distance problem, to realize the effective use of mine monitoring wind speed data. The experiments on public KEEL datasets verify that the one-class classification integration model can solve the multiclassification problem and that the MC-OCSVM model has better generalizability than other one-class classification integration models do. The experiment is carried out in the Buertai coal mine, and the results show that the proposed algorithm can identify fault branches quickly and accurately, with an accuracy of 93.2% and a single fault diagnosis time is 1.2 s, highlighting its strong robustness.