使用CNN进行轴承振动分析以防止故障
Vibration Analysis in Bearings for Failure Prevention using CNN
摘要：
轴承故障的及时检测对于防止工业上的经济损失具有重要意义。在这篇文章中，我们提出了一种基于卷积神经网络（CNN）的方法来估计轴承的磨损程度。首先，对原始振动数据进行自动标注，获得不同程度的轴承磨损，利用均方根特征和香农熵从原始数据中提取特征，然后利用K均值算法将原始数据分成7个不同的类，得到不同程度的轴承磨损的标注。然后，原始振动数据被转换成小的正方形图像，每个数据样本代表图像的一个像素。在此之后，我们提出了一个CNN模型的基础上的AlexNet架构分类的磨损水平和诊断的旋转系统。为了训练网络并验证我们的建议，我们使用来自智能维护系统（IMS）中心的数据集，并将其与文献中报道的其他方法进行了广泛的比较。所提出的策略的有效性被证明是优秀的，优于其他方法在国家的最先进的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
