Multi-modal data cross-domain fusion network for gearbox fault diagnosis under variable operating conditions

计算机科学 情态动词 变量(数学) 领域(数学分析) 断层(地质) 融合 数据挖掘 实时计算 地质学 数学 语言学 数学分析 哲学 地震学 化学 高分子化学
作者
Yongchao Zhang,Jinliang Ding,Yongbo Li,Zhaohui Ren,Ke Feng
出处
期刊:Engineering Applications of Artificial Intelligence [Elsevier BV]
卷期号:133: 108236-108236 被引量:46
标识
DOI:10.1016/j.engappai.2024.108236
摘要

Gearbox fault diagnosis is a critical aspect of machinery maintenance and reliability, as it ensures the safe and efficient operation of various industrial systems. The cross-domain fault diagnosis method based on transfer learning has been extensively researched to enhance the engineering applications of data-driven methods. Currently, the state-of-the-art gearbox cross-domain fault diagnosis primarily relies on single-modal data, which may not capture the full information needed for robust fault diagnosis under varying conditions. To address this issue, we propose a novel multi-modal data cross-domain fusion network that utilizes vibration signals and thermal images to capture comprehensive information about the gearbox's health conditions. First, one-dimensional and two-dimensional convolutional neural networks are constructed for feature extraction and fusion of multi-modal data. Then, the Maximum Mean Discrepancy loss is introduced to achieve cross-domain feature alignments within the modal. Finally, the cross-modal consistency learning strategy is constructed to enhance the cross-domain diagnosis performance of the model. To validate the effectiveness of the proposed method, we conducted experiments on a real-world gearbox test rig. Experimental results demonstrate that the proposed method is superior to single-modal methods and existing fusion methods in terms of diagnosis performance, proving that the proposed method offers a promising solution for gearbox fault diagnosis in industrial settings.
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