Mao Yulin, Xin Jianghui, Zang Liguo, Jiao Jing, Xue Cheng
School of Automotive and Rail Transit, Nanjing Institute of Technology, Nanjing 211167, China.
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130015, China.
Entropy (Basel). 2024 Feb 29;26(3):222. doi: 10.3390/e26030222.
Aiming at the difficult problem of extracting fault characteristics and the low accuracy of fault diagnosis throughout the full life cycle of rolling bearings, a fault diagnosis method for rolling bearings based on grey relation degree is proposed in this paper. Firstly, the subtraction-average-based optimizer is used to optimize the parameters of the variational mode decomposition algorithm. Secondly, the vibration signals of bearings are decomposed by using the optimized results, and the feature vector of the intrinsic mode function component corresponding to the minimum envelope entropy is extracted. Finally, the grey proximity and similarity relation degree based on standard distance entropy are weighted to calculate the grey comprehensive relation degree between the feature vector of vibration signals and each standard state. By comparing the results, the diagnosis of different fault states and degrees of rolling bearings is realized. The XJTU-SY dataset was used for experimentation, and the results show that the proposed method achieves a diagnostic accuracy of 95.24% and has better diagnosis performance compared to various algorithms. It provides a reference for the fault diagnosis of rolling bearings throughout the full life cycle.
针对滚动轴承全寿命周期内故障特征提取困难、故障诊断准确率低的问题，本文提出一种基于灰色关联度的滚动轴承故障诊断方法。首先，采用基于减法平均的优化器对变分模态分解算法的参数进行优化。其次，利用优化结果对轴承振动信号进行分解，提取包络熵最小的本征模态函数分量的特征向量。最后，基于标准距离熵对灰色贴近度和相似度进行加权，计算振动信号特征向量与各标准状态之间的灰色综合关联度。通过比较结果，实现对滚动轴承不同故障状态和程度的诊断。采用XJTU-SY数据集进行实验，结果表明，所提方法的诊断准确率达到95.24%，与各种算法相比具有更好的诊断性能。为滚动轴承全寿命周期的故障诊断提供了参考。
