论文标题
使用CNN预防故障的轴承中的振动分析
Vibration Analysis in Bearings for Failure Prevention using CNN
论文作者
论文摘要
轴承的及时失败检测对于防止行业的经济损失至关重要。在本文中,我们提出了一种基于卷积神经网络(CNN)的方法,以估计轴承的磨损水平。首先,通过根平方的特征以及香农的熵,从原始数据中提取特征,然后使用K-MEANS算法将其分组,从而通过Root Means Square以及Shannon的熵进行自动标记,以获得不同级别的轴承磨损,以获取原始数据的特征。然后,将原始振动数据转换为小正方形图像,每个数据的每个样本代表图像的一个像素。之后,我们提出了一个基于Alexnet体系结构的CNN模型,以对磨损水平进行分类并诊断旋转系统。为了培训网络并验证我们的建议,我们使用了智能维护系统(IMS)中心的数据集,并将其与文献中报告的其他方法进行了广泛的比较。事实证明,拟议策略的有效性是出色的,在最先进的方法中表现优于其他方法。
Timely failure detection for bearings is of great importance to prevent economic loses in the industry. In this article we propose a method based on Convolutional Neural Networks (CNN) to estimate the level of wear in bearings. First of all, an automatic labeling of the raw vibration data is performed to obtain different levels of bearing wear, by means of the Root Mean Square features along with the Shannon's entropy to extract features from the raw data, which is then grouped in seven different classes using the K-means algorithm to obtain the labels. Then, the raw vibration data is converted into small square images, each sample of the data representing one pixel of the image. Following this, we propose a CNN model based on the AlexNet architecture to classify the wear level and diagnose the rotatory system. To train the network and validate our proposal, we use a dataset from the center of Intelligent Maintenance Systems (IMS), and extensively compare it with other methods reported in the literature. The effectiveness of the proposed strategy proved to be excellent, outperforming other approaches in the state-of-the-art.