College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Comput Intell Neurosci. 2022 Apr 25;2022:1615676. doi: 10.1155/2022/1615676. eCollection 2022.
In bearing fault diagnosis, due to the insufficient obtained supervised data and the inevitable noise contained in the vibration signals, the problem of clustering bearing fault diagnosis with imbalanced data containing noise is caused. Thanks to the ability to quickly and fully learn boundary information in small samples, the extension neural network-type 2 algorithm (ENN-2) has the potential in imbalanced data clustering and has been gradually applied in fault diagnosis. Therefore, in order to improve the unstable clustering performance of ENN-2 caused by its heavy dependence on input order of samples, a novel algorithm called linked extension neural network (LENN) is developed by redesigning the correlation function and its iterative method, which greatly reduces the clustering iteration epochs of the algorithm. In addition, an evaluation index of clustering quality for this novel algorithm, extension density, is also proposed. After that, a bearing fault diagnosis model of variational mode decomposition (VMD) based denoising and LENN is proposed. Firstly, VMD is used to get intrinsic mode functions (IMFs), and the correlation coefficients of IMFs are calculated for signal denoising. Secondly, the features are extracted from denoised signals and selected by PCA algorithm, and the fault diagnosis is finally completed by LENN. Compared with ENN-2, K-means, FCM, and DBSCAN based models, the proposed model identifies the faults with different severities more accurately and achieves superior diagnostic ability on different imbalance degrees of datasets, which can further lay a foundation for clustering fault diagnosis based on vibration signals.
在轴承故障诊断中，由于获得的监督数据不足和振动信号中不可避免的噪声，导致存在包含噪声的不平衡数据聚类轴承故障诊断的问题。由于扩展神经网络类型 2 算法（ENN-2）具有快速全面学习小样本边界信息的能力，因此在不平衡数据聚类中具有潜力，并已逐渐应用于故障诊断。因此，为了提高 ENN-2 对样本输入顺序的严重依赖导致的不稳定聚类性能，通过重新设计相关函数及其迭代方法，开发了一种称为链接扩展神经网络（LENN）的新型算法，极大地减少了算法的聚类迭代周期。此外，还提出了一种用于该新型算法的聚类质量评估指标，即扩展密度。然后，提出了基于变分模态分解（VMD）降噪和 LENN 的轴承故障诊断模型。首先，使用 VMD 获得固有模态函数（IMF），并计算 IMF 的相关系数以进行信号降噪。其次，从去噪信号中提取特征，并通过 PCA 算法进行选择，最后通过 LENN 完成故障诊断。与 ENN-2、K-means、FCM 和 DBSCAN 模型相比，所提出的模型能够更准确地识别不同严重程度的故障，并且在不同数据集的不平衡程度上具有卓越的诊断能力，这可以为基于振动信号的聚类故障诊断进一步奠定基础。
