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

• Published in: Engineering applications of artificial intelligence 2024-07, Vol.133, p.108236, Article 108236
• Main Authors: Zhang, Yongchao, Ding, Jinliang, Li, Yongbo, Ren, Zhaohui, Feng, Ke
• Format: Article
• Language: English
• Subjects: Cross-domain; Fault diagnosis; Gearbox; Multi-modal data; Transfer learning
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2024.108236

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.