4.6 Article

Improved Variational Mode Decomposition and One-Dimensional CNN Network with Parametric Rectified Linear Unit (PReLU) Approach for Rolling Bearing Fault Diagnosis

期刊

APPLIED SCIENCES-BASEL
卷 12, 期 18, 页码 -

出版社

MDPI
DOI: 10.3390/app12189324

关键词

bearing fault diagnosis; grey wolf optimization (GWO); variational modal decomposition (VMD); convolutional neural network (CNN); parametric rectified linear unit (PReLU)

资金

  1. National Natural Science Foundation of China [62001262, 62001263]
  2. Natural Science Foundation of Shandong Province [ZR2020QF008]

向作者/读者索取更多资源

Rolling bearings are crucial for safe and efficient operation of machinery, but faults can lead to downtime and economic costs. A model based on improved grey wolf optimization and improved 1DCNN is proposed to extract fault features. Experimental results show the effectiveness and high recognition accuracy of the model.
As a critical component of rotating machinery, rolling bearings are essential for the safe and efficient operation of machinery. Sudden faults of rolling bearings can lead to unscheduled downtime and substantial economic costs. Therefore, diagnosing and identifying the equipment status is essential for ensuring the operation and decreasing the additional maintenance costs of the machines. However, extracting the features from the early bearing fault signals is challenging under background noise interference. With the purpose of solving the above problem, we propose an integrated rolling bearing fault diagnosis model based on the improved grey wolf optimized variational modal decomposition (IGVMD) and an improved 1DCNN with a parametric rectified linear unit (PReLU). Firstly, an improved grey wolf optimizer (IGWO) with the fitness function, the minimum envelope entropy, is designed for adaptively searching the optimal parameter values of the VMD model. The performance of the basic grey wolf optimizer (GWO) algorithm by introducing three improvement strategies, the non-linear convergence factor adjustment strategy, the grey wolf adaptive position update strategy, and the Levy flight strategy in the IGWO algorithm, is improved. Then, an improved 1DCNN model with the PReLU activation function is proposed, which extracts the bearing fault features, and a grid search to optimize the model parameters of the 1DCNN is introduced. Finally, the effectiveness of the proposed model is demonstrated well by employing two experimental datasets. The preliminary comparative results of the average identification accuracy in the proposed method in two datasets are 99.98% and 99.50%, respectively, suggesting that this proposed method has a relatively higher recognition accuracy and application values.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据