Wang Jingcan, Yuan Yiping, Shen Fangqi, Chen Caifeng
School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China.
School of Business, University of Leeds, Leeds LS2 9JT, UK.
Sensors (Basel). 2025 Jul 4;25(13):4168. doi: 10.3390/s25134168.
As the mining motor is used long-term in a complex multi-source noise environment composed of equipment group coordinated operations and high-frequency start-stop, its vibration signal has the features of significant strong noise interference, weak fault features, and the superposition of multiple working conditions coupling, which makes it arduous to efficiently extract and identify mechanical fault features. To address this issue, this study introduces a high-performance fault diagnosis approach for mining motors operating under strong background noise by integrating parameter-optimized feature mode decomposition (WOA-FMD) with the RepLKNet-BiGRU-Attention dual-channel model. According to the experimental results, the average accuracies of the proposed method were 97.7% and 93.38% for the noise-added CWRU bearing fault dataset and the actual operation dataset of the mine motor, respectively, which are significantly better than those of similar methods, showing that the approach in this study is superior in fault feature extraction and identification.
由于矿用电机长期运行在由设备群协同作业和高频启停组成的复杂多源噪声环境中，其振动信号具有强噪声干扰显著、故障特征微弱以及多工况耦合叠加的特点，这使得高效提取和识别机械故障特征变得十分困难。为解决这一问题，本研究通过将参数优化的特征模式分解（WOA-FMD）与RepLKNet-BiGRU-Attention双通道模型相结合，引入了一种在强背景噪声下运行的矿用电机高性能故障诊断方法。根据实验结果，该方法在添加噪声的CWRU轴承故障数据集和矿用电机实际运行数据集上的平均准确率分别为97.7%和93.38%，显著优于同类方法，表明本研究中的方法在故障特征提取和识别方面具有优越性。
