论文标题
滚珠轴承中的故障检测
Fault Detection in Ball Bearings
论文作者
论文摘要
滚珠轴承接头是所有旋转机械的关键组成部分，在这些关节中检测和定位故障是行业和研究中的一个重大问题。智能故障检测（IFD）是应用机器学习和其他统计方法来监视机器健康状态的过程。本文探讨了振动图像的构建，这是一种预处理技术，以前已用于训练卷积神经网络，用于滚珠轴承关节IFD。主要结果证明了该技术的鲁棒性，将其应用于比以前使用的更大的数据集中，并探索用于构建振动图像的超参数。
Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset than previously used and exploring the hyperparameters used in constructing the vibration images.
