School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel). 2023 May 21;23(10):4951. doi: 10.3390/s23104951.
This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)-Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.
本文介绍了一种利用电机电流特征分析（MCSA）对矿用刮板输送机齿轮进行故障诊断的方法。该方法解决了因受煤流负载和功率频率影响而难以有效提取的齿轮故障特征相关问题。提出了一种基于变分模态分解（VMD）-希尔伯特谱和 ShuffleNet-V2 的故障诊断方法。首先，利用 VMD 将齿轮电流信号分解为一系列固有模态函数（IMF），并利用遗传算法（GA）对 VMD 的敏感参数进行优化。敏感 IMF 算法判断 VMD 处理后对故障信息敏感的模态函数。通过对故障敏感 IMF 的局部希尔伯特瞬时能谱进行分析，得到信号能量随时间变化的准确表达式，生成不同故障齿轮的局部希尔伯特瞬时能谱数据集。最后，利用 ShuffleNet-V2 识别齿轮故障状态。实验结果表明，ShuffleNet-V2 神经网络在 778 s 后达到 91.66%的准确率。
