Liu Ruozhu, Wang Xingbing, Kumar Anil, Sun Bintao, Zhou Yuqing
School of International Education, Jiaxing Nanyang Polytechnic Institute, Jiaxing 314000, China.
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China.
Micromachines (Basel). 2023 Jul 21;14(7):1467. doi: 10.3390/mi14071467.
Rolling bearings are crucial mechanical components in the mechanical industry. Timely intervention and diagnosis of system faults are essential for reducing economic losses and ensuring product productivity. To further enhance the exploration of unlabeled time-series data and conduct a more comprehensive analysis of rolling bearing fault information, this paper proposes a fault diagnosis technique for rolling bearings based on graph node-level fault information extracted from 1D vibration signals. In this technique, 10 categories of 1D vibration signals from rolling bearings are sampled using a sliding window approach. The sampled data is then subjected to wavelet packet decomposition (WPD), and the wavelet energy from the final layer of the four-level WPD decomposition in each frequency band is used as the node feature. The weights of edges between nodes are calculated using the Pearson correlation coefficient (PCC) to construct a node graph that describes the feature information of rolling bearings under different health conditions. Data augmentation of the node graph in the dataset is performed by randomly adding nodes and edges. The graph convolutional neural network (GCN) is employed to encode the augmented node graph representation, and deep graph contrastive learning (DGCL) is utilized for the pre-training and classification of the node graph. Experimental results demonstrate that this method outperforms contrastive learning-based fault diagnosis methods for rolling bearings and enables rapid fault diagnosis, thus ensuring the normal operation of mechanical systems. The proposed WPDPCC-DGCL method offers two advantages: (1) the flexibility of wavelet packet decomposition in handling non-smooth vibration signals and combining it with the powerful multi-scale feature encoding capability of GCN for richer characterization of fault information, and (2) the construction of graph node-level fault samples to effectively capture underlying fault information. The experimental results demonstrate the superiority of this method in rolling bearing fault diagnosis over contrastive learning-based approaches, enabling fast and accurate fault diagnoses for rolling bearings and ensuring the normal operation of mechanical systems.
滚动轴承是机械工业中至关重要的机械部件。及时对系统故障进行干预和诊断对于减少经济损失和确保产品生产率至关重要。为了进一步加强对未标记时间序列数据的探索，并对滚动轴承故障信息进行更全面的分析，本文提出了一种基于从一维振动信号中提取的图节点级故障信息的滚动轴承故障诊断技术。在该技术中，使用滑动窗口方法对滚动轴承的10类一维振动信号进行采样。然后对采样数据进行小波包分解（WPD），将四级WPD分解最后一层在每个频带的小波能量用作节点特征。使用皮尔逊相关系数（PCC）计算节点之间边的权重，以构建描述不同健康状态下滚动轴承特征信息的节点图。通过随机添加节点和边对数据集中的节点图进行数据增强。采用图卷积神经网络（GCN）对增强后的节点图表示进行编码，并利用深度图对比学习（DGCL）对节点图进行预训练和分类。实验结果表明，该方法优于基于对比学习的滚动轴承故障诊断方法，能够实现快速故障诊断，从而确保机械系统的正常运行。所提出的WPDPCC-DGCL方法具有两个优点：（1）小波包分解在处理非平稳振动信号方面的灵活性，并将其与GCN强大的多尺度特征编码能力相结合，以更丰富地表征故障信息；（2）构建图节点级故障样本以有效捕获潜在故障信息。实验结果证明了该方法在滚动轴承故障诊断中优于基于对比学习的方法，能够对滚动轴承进行快速准确的故障诊断，并确保机械系统的正常运行。
