Fair Friend Institute of Intelligent Manufacturing, Hangzhou Vocational and Technical College, Hangzhou 310018, China.
Comput Intell Neurosci. 2022 Aug 28;2022:9231305. doi: 10.1155/2022/9231305. eCollection 2022.
In the field of mechanical and electrical equipment, the motor rolling bearing is a workpiece that is extremely prone to damage and failure. However, the traditional fault diagnosis methods cannot keep up with the development pace of the times because they need complex manual pretreatment or the support of specific expert experience and knowledge. As a rising star, the data-driven fault diagnosis methods are increasingly favored by scholars and experts at home and abroad. The convolutional neural network has been widely used because of its powerful feature extraction ability for all kinds of complex information and its outstanding research results in image processing, target tracking, target diagnosis, time-frequency analysis, and other scenes. Therefore, this paper introduces a convolutional neural network and applies it to motor-bearing fault diagnosis. Aiming at the shortcomings of fault signal and convolutional neural network, a large-scale maximum pooling strategy is proposed and optimized by wavelet transform to improve the fault diagnosis efficiency of motor bearing under high-voltage operation. Compared with other machine learning algorithms, the convolution neural network fault diagnosis model constructed in this paper not only has high accuracy (up to 0.9871) and low error (only 0.032) but also is simple to use. It provides a new way for motor bearing fault diagnosis and has very important economic and social value.
在机电设备领域，电机滚动轴承是一种极易损坏和失效的工件。然而，传统的故障诊断方法由于需要复杂的人工预处理或特定专家经验和知识的支持，已经跟不上时代的发展步伐。作为一颗冉冉升起的新星，数据驱动的故障诊断方法越来越受到国内外学者和专家的青睐。卷积神经网络由于其对各种复杂信息的强大特征提取能力以及在图像处理、目标跟踪、目标诊断、时频分析等场景中的出色研究成果，得到了广泛的应用。因此，本文引入了卷积神经网络，并将其应用于电机轴承故障诊断。针对故障信号和卷积神经网络的缺点，提出了一种基于小波变换的大规模最大池化策略，并进行了优化，以提高高压运行下电机轴承的故障诊断效率。与其他机器学习算法相比，本文构建的卷积神经网络故障诊断模型不仅具有高精度（高达 0.9871）和低误差（仅 0.032），而且使用简单。它为电机轴承故障诊断提供了一种新的途径，具有非常重要的经济和社会价值。
