基于IBWO-VMD与CNN-BiLSTM的磁力耦合器轴承故障诊断方法

Magnetic Coupler Bearing Fault Diagnosis Method Based on IBWO-VMD and CNN-BiLSTM

摘要:

针对磁力耦合器轴承故障信号微弱、特征提取困难导致故障分类准确率低的问题,提出一种改进的白鲸优化算法(IBWO)优化变分模态分解(VMD),并结合卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)组合模型的滚动轴承故障诊断方法。首先使用改进的白鲸优化算法寻优VMD的两个重要参数(模态数目K和惩罚因子α),然后将寻优得到的两个参数代入VMD可以获得K个模态分量(IMF),选择包络熵最小的IMF分量作为有效分量,最后将该分量输入到CNN-BiLSTM模型中进行故障诊断。分别使用凯斯西储大学以及渥太华大学公开数据集进行实验,结果表明,该模型故障识别准确率均达95%以上,证明所提出的诊断方法在识别准确率方面具有明显优势,研究结果可为磁力耦合器轴承的故障诊断提供参考。

Abstract:

Aiming at the problem of weak fault signals of magnetic coupler bearings and the difficulty of feature extraction, which leads to the low accuracy of fault classification, a rolling bearing fault diagnosis method is proposed, which uses improved beluga whale optimization (IBWO) to optimize variational mode decomposition (VMD), and combines the hybrid model of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Firstly, the IBWO algorithm is used to optimize the two key parameters of VMD (mode number K and penalty factor α). Then, the two optimized parameters are substituted into VMD to obtain K intrinsic mode functions (IMFs). Next, the IMF component with the minimum envelope entropy is selected as the effective IMF component, which is finally input into the CNN-BiLSTM model for fault diagnosis. Experiments are conducted using the public datasets from the Case Western Reserve University and the University of Ottawa, respectively. The results show that the fault identification accuracy of the proposed model can reach more than 95%, which proves that the present diagnosis method has a significant advantage in identification accuracy. The research results can provide a reference for the fault diagnosis of magnetic coupler bearings.