A novel fault diagnosis model based on deep feature fusion network under imbalanced data: Towards railway dual-switch machines traction occasion

牵引(地质) 对偶(语法数字) 计算机科学 特征(语言学) 断层(地质) 融合 人工智能 数据挖掘 模式识别(心理学) 地质学 工程类 机械工程 地震学 艺术 语言学 哲学 文学类
作者
Shuai Xiao,Qingsheng Feng,Xiaoxi Hu,Yakun Song,Guanglin Cong,Zhuoyi Yao,Hong Li
出处
期刊:Measurement Science and Technology [IOP Publishing]
标识
DOI:10.1088/1361-6501/ad9514
摘要

Abstract Fault diagnosis of railway switch machines is crucial for ensuring safe and efficient train operations, as well as for the maintenance of intelligent Switching & Crossing systems. Current methods primarily focus on single-switch machine traction modes, often overlooking the challenges of effectively utilizing multi-source data and comprehensively representing fault information. This limitation results in restricted applicability and suboptimal recognition accuracy. To address these challenges, we propose a novel fault diagnosis model based on a deep feature fusion network (DFFN) specifically designed for railway dual-switch machines (RDSMs) in traction occasion, particularly under imbalanced data conditions. First, we introduce an improved synthetic minority oversampling technique (ISMOTE) that integrates clustering technology with neighbor-based strategies to balance the experimental data and mitigate training bias. Second, we incorporate a cross-branch convolutional collaborative self-attention mechanism network (CBCSAMN) and an adaptive weight learning network (AWLN) into the DFFN, facilitating the extraction of multi-scale fault feature correlations and promoting efficient fusion. Experimental results, based on multiple vibration sensing signals, demonstrate an average diagnostic accuracy of 96.66% and an F1-score of 96.85% in real railway environments. Comparative analyses with other state-of-the-art methods confirm that our approach achieves superior diagnostic performance.
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