基于卷积神经网络的机械故障诊断技术综述

针对传统机械故障诊断方法难以解决人工提取不确定性的问题，提出了大量深度学习的特征提取方法，极大地推动了机械故障诊断的发展。作为深度学习的典型代表，卷积神经网络（CNN）在图像分类、目标检测、图像语义分割等领域都取得了重大的发展，在机械故障诊断领域也有大量文献发表。为了进一步了解利用CNN的方法进行机械故障诊断的问题，首先简单介绍了CNN的相关理论，然后从数据输入类型、迁移学习、预测等方面对CNN在机械故障诊断中的应用进行了归纳总结，最后展望了CNN及其在机械故障诊断应用中的发展方向。

Review of mechanical fault diagnosis technology based on convolutional neural network

In view of the difficulty of traditional mechanical fault diagnosis methods to solve the problem of the uncertainty of manual extraction， a large number of deep learning feature extraction methods have been proposed， which greatly promotes the development of mechanical fault diagnosis. As a typical representative of deep learning， convolution neural networks have made significant developments in image classification， target detection， image semantic segmentation and other fields. There is also a lot of literature in the field of mechanical fault diagnosis. In view of the published literature， in order to further understand the problem of mechanical fault diagnosis by using the method of convolutional neural network， on the basis of a brief introduction to the relevant theories of convolution neural network， and then from the aspects such as data input type， transfer learning， and prediction， the applications of convolution neural network in mechanical fault diagnosis were summarized. Finally， the development directions of convolution neural network and its applications in mechanical fault diagnosis were prospected.