Wang Yu, Li Dexiong, Li Lei, Sun Runde, Wang Shuqing
Department of Electrical Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang, 050041, China.
Heliyon. 2024 Jul 30;10(15):e35407. doi: 10.1016/j.heliyon.2024.e35407. eCollection 2024 Aug 15.
In the context of burgeoning industrial advancement, there is an increasing trend towards the integration of intelligence and precision in mechanical equipment. Central to the functionality of such equipment is the rolling bearing, whose operational integrity significantly impacts the overall performance of the machinery. This underscores the imperative for reliable fault diagnosis mechanisms in the continuous monitoring of rolling bearing conditions within industrial production environments. Vibration signals are primarily used for fault diagnosis in mechanical equipment because they provide comprehensive information about the equipment's condition. However, fault data often contain high noise levels, high-frequency variations, and irregularities, along with a significant amount of redundant information, like duplication, overlap, and unnecessary information during signal transmission. These characteristics present considerable challenges for effective fault feature extraction and diagnosis, reducing the accuracy and reliability of traditional fault detection methods. This research introduces an innovative fault diagnosis methodology for rolling bearings using deep convolutional neural networks (CNNs) enhanced with variational autoencoders (VAEs). This deep learning approach aims to precisely identify and classify faults by extracting detailed vibration signal features. The VAE enhances noise robustness, while the CNN improves signal data expressiveness, addressing issues like gradient vanishing and explosion. The model employs the reparameterization trick for unsupervised learning of latent features and further trains with the CNN. The system incorporates adaptive threshold methods, the "3/5" strategy, and Dropout methods. The diagnosis accuracy of the VAE-CNN model for different fault types at different rotational speeds typically reaches more than 90 %, and it achieves a generally acceptable diagnosis result. Meanwhile, the VAE-CNN augmented fault diagnosis model, after experimental validation in various dimensions, can achieve more satisfactory diagnosis results for various fault types compared to several representative deep neural network models without VAE augmentation, significantly improving the accuracy and robustness of rolling bearing fault diagnosis.
在工业蓬勃发展的背景下，机械设备智能化与精密化集成的趋势日益明显。滚动轴承是此类设备功能的核心，其运行完整性对机械的整体性能有重大影响。这凸显了在工业生产环境中持续监测滚动轴承状态时，建立可靠故障诊断机制的必要性。振动信号主要用于机械设备的故障诊断，因为它们能提供有关设备状态的全面信息。然而，故障数据往往包含高噪声水平、高频变化和不规则性，以及大量冗余信息，如信号传输过程中的重复、重叠和不必要信息。这些特性给有效的故障特征提取和诊断带来了巨大挑战，降低了传统故障检测方法的准确性和可靠性。本研究引入了一种创新的滚动轴承故障诊断方法，该方法使用了结合变分自编码器（VAE）的深度卷积神经网络（CNN）。这种深度学习方法旨在通过提取详细的振动信号特征来精确识别和分类故障。VAE增强了噪声鲁棒性，而CNN提高了信号数据的表现力，解决了梯度消失和爆炸等问题。该模型采用重参数化技巧对潜在特征进行无监督学习，并进一步与CNN一起训练。该系统采用了自适应阈值方法、“3/5”策略和随机失活方法。VAE-CNN模型在不同转速下对不同故障类型的诊断准确率通常达到90%以上，取得了普遍可接受的诊断结果。同时，经过多维度实验验证，与几种无VAE增强的代表性深度神经网络模型相比，VAE-CNN增强故障诊断模型对各种故障类型都能取得更满意的诊断结果，显著提高了滚动轴承故障诊断的准确性和鲁棒性。
