Cui Wei, Ding Jun, Meng Guoying, Lv Zhengyan, Feng Yahui, Wang Aiming, Wan Xingwei
School of Mechanical Electronic & Information Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China.
School of Emergency Equipment, North China Institute of Science and Technology, Langfang 065201, China.
Entropy (Basel). 2023 Aug 18;25(8):1233. doi: 10.3390/e25081233.
Rolling bearings are crucial parts of primary mine fans. In order to guarantee the safety of coal mine production, primary mine fans commonly work during regular operation and are immediately shut down for repair in case of failure. This causes the sample imbalance phenomenon in fault diagnosis (FD), i.e., there are many more normal state samples than faulty ones, seriously affecting the precision of FD. Therefore, the current study presents an FD approach for the rolling bearings of primary mine fans under sample imbalance conditions via symmetrized dot pattern (SDP) images, denoising diffusion probabilistic models (DDPMs), the image generation method, and a convolutional neural network (CNN). First, the 1D bearing vibration signal was transformed into an SDP image with significant characteristics, and the DDPM was employed to create a generated image with similar feature distributions to the real fault image of the minority class. Then, the generated images were supplemented into the imbalanced dataset for data augmentation to balance the minority class samples with the majority ones. Finally, a CNN was utilized as a fault diagnosis model to identify and detect the rolling bearings' operating conditions. In order to assess the efficiency of the presented method, experiments were performed using the regular rolling bearing dataset and primary mine fan rolling bearing data under actual operating situations. The experimental results indicate that the presented method can more efficiently fit the real image samples' feature distribution and generate image samples with higher similarity than other commonly used methods. Moreover, the diagnostic precision of the FD model can be effectively enhanced by gradually expanding and enhancing the unbalanced dataset.
滚动轴承是煤矿主通风机的关键部件。为了保证煤矿生产安全，煤矿主通风机在正常运行时通常持续工作，一旦出现故障便立即停机维修。这就导致了故障诊断中的样本不均衡现象，即正常状态样本比故障样本多得多，严重影响了故障诊断的精度。因此，当前的研究提出了一种在样本不均衡条件下针对煤矿主通风机滚动轴承的故障诊断方法，该方法通过对称点模式（SDP）图像、去噪扩散概率模型（DDPM）、图像生成方法和卷积神经网络（CNN）来实现。首先，将一维轴承振动信号转换为具有显著特征的SDP图像，并使用DDPM创建一个生成图像，其特征分布与少数类别的真实故障图像相似。然后，将生成的图像补充到不均衡数据集中进行数据增强，以使少数类样本与多数类样本达到平衡。最后，利用卷积神经网络作为故障诊断模型来识别和检测滚动轴承的运行状态。为了评估所提方法的有效性，使用常规滚动轴承数据集以及实际运行情况下的煤矿主通风机滚动轴承数据进行了实验。实验结果表明，所提方法能够更有效地拟合真实图像样本的特征分布，并且比其他常用方法生成相似度更高的图像样本。此外，通过逐步扩展和增强不均衡数据集，可以有效提高故障诊断模型的诊断精度。
