Kou Ziming, Yang Fen, Wu Juan, Li Tengyu
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
School of Mechanical Engineering, North University of China, Taiyuan 030051, China.
Entropy (Basel). 2020 Nov 28;22(12):1347. doi: 10.3390/e22121347.
The mine hoist sheave bearing is a large heavy-duty bearing, located in a derrick of tens of meters. Aiming at the difficulty of sheave bearing fault diagnosis, a combined fault-diagnosis method based on the improved complete ensemble EMD (ICEEMDAN) energy entropy and support vector machine (SVM) optimized by artificial fish swarm algorithm (AFSA) was proposed. Different location of the bearing defect will result in different frequency components and different amplitude energy of the frequency. According to this feature, the position of the bearing defect can be determined by calculating the ICEEMDAN energy entropy of different vibration signals. In view of the difficulty in selecting the penalty factor and radial basis kernel parameter in the SVM model, the AFSA was used to optimize them. The experimental results show that the accuracy rate of the optimized fault-diagnosis model is improved by 10% and the diagnostic accuracy rate is 97.5%.
矿井提升机天轮轴承是一种大型重载轴承，位于数十米高的井架中。针对天轮轴承故障诊断的难题，提出了一种基于改进的完备总体经验模态分解（ICEEMDAN）能量熵和人工鱼群算法（AFSA）优化的支持向量机（SVM）的联合故障诊断方法。轴承缺陷位置不同，会导致频率成分不同，频率的幅值能量也不同。根据这一特征，通过计算不同振动信号的ICEEMDAN能量熵来确定轴承缺陷的位置。针对支持向量机模型中惩罚因子和径向基核参数选择困难的问题，采用人工鱼群算法对其进行优化。实验结果表明，优化后的故障诊断模型准确率提高了10%，诊断准确率为97.5%。
