Cao Weilin, Zhang Liqiang
School of Artificial Intelligence, Neijiang Normal University, Sichuan, China.
PLoS One. 2025 Jun 26;20(6):e0326666. doi: 10.1371/journal.pone.0326666. eCollection 2025.
Rolling bearings are the core transmission components of large-scale rotating machinery such as wind power gearboxes and aviation engines, so timely and effective monitoring and diagnosis of their status are crucial to ensure the stable operation of equipment, reduce maintenance costs, and improve production efficiency. However, the noise interference in the industrial field often hides the original characteristics of the bearing fault signal, leading to the deep learning-based fault diagnosis model's lack of diagnostic reliability in the strong industrial noise background. To address this problem, this paper proposes a multi-domain collaborative denoising diagnostic model based on dynamic inter-domain attention mechanism and noise-aware loss function. First, the model extracts high-dimensional features of bearing fault signals from multiple domains, such as time and frequency domains, aiming to enhance the richness and diversity of high-dimensional features to effectively suppress noise interference on the diagnostic results. Second, the dynamic inter-domain attention mechanism (DIDAM) is proposed, aiming to distinguish the importance of information in different signal domains and flexibly integrate them to realize more efficient and accurate multi-domain information fusion. Finally, the noise-aware loss function (NALF) is designed to avoid the phenomenon of the conduction model being prone to making wrong decisions due to excessive noise. Experimental results on two publicly available datasets, CWRU and MFPT, show that even in the extreme noise environment with SNR = -10 dB, the proposed model still achieves 81.25% and 76.36% fault diagnosis accuracies, which are better than most existing mainstream denoising models. Overall, the proposed method can still perform well under substantial noise interference, providing a new idea for intelligent bearing fault diagnosis in real industrial scenarios.
滚动轴承是风力发电齿轮箱和航空发动机等大型旋转机械的核心传动部件，因此及时有效地监测和诊断其状态对于确保设备稳定运行、降低维护成本以及提高生产效率至关重要。然而，工业现场的噪声干扰常常掩盖了轴承故障信号的原始特征，导致基于深度学习的故障诊断模型在强工业噪声背景下缺乏诊断可靠性。针对这一问题，本文提出了一种基于动态域间注意力机制和噪声感知损失函数的多域协同去噪诊断模型。首先，该模型从时间和频率域等多个域中提取轴承故障信号的高维特征，旨在增强高维特征的丰富性和多样性，以有效抑制噪声对诊断结果的干扰。其次，提出了动态域间注意力机制（DIDAM），旨在区分不同信号域中信息的重要性，并灵活地将它们整合起来，以实现更高效、准确的多域信息融合。最后，设计了噪声感知损失函数（NALF），以避免传导模型因噪声过大而容易做出错误决策的现象。在两个公开数据集CWRU和MFPT上的实验结果表明，即使在信噪比SNR = -10 dB的极端噪声环境下，所提出的模型仍分别实现了81.25%和76.36%的故障诊断准确率，优于大多数现有的主流去噪模型。总体而言，所提出的方法在大量噪声干扰下仍能表现良好，为实际工业场景中的智能轴承故障诊断提供了新思路。
