On-line process monitoring increases product quality, improves process stability, and lowers costs in manufacturing. This paper presents a study of using time-frequency representation and deep neural networks to enable real-time, intelligent manufacturing process monitoring. Acoustic emission (AE) signals are obtained during machine turning operations, and transformed into a time-frequency (TF) representation (image) format. Deep neural networks are then applied to the images for process classification according to the operation's spindle speed, feed rate, and depth of cut. The signals are nonstationary with frequency content varying in time. Four commonly used time-frequency analysis methods are discussed and compared, such as the short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert-Huang transform (HHT), and Wigner–Ville distribution (WVD). The study finds that the multi-resolution capability of the CWT technique allows it to render more accurate and richer details of the signals. Classification of machine-turning-operation processes through conventional monitoring techniques is challenging. First, there are not theories directly relating the signal characteristics to the physical process. Also, important signal components associated with the physics of the process are masked by heavy noise from the lathe. These lead to difficulties in selecting features to be extracted, and obtaining reliable data of these features. These obstacles are overcome by deep neural networks that are capable of learning and extracting meaningful features in an automatous fashion. A transfer learning approach is adopted in this study by using high-performance, representative deep neural networks previously developed for image classification and recognition, including ShuffleNet, GoogLeNet, ResNet18, ResNet50, VGG16, and DenseNet201. They are applied to the TF images obtained during the machine turning operations for process classification. The VGG16 network yields the highest classification accuracy at 92.67% when applied to processes of 12 classes, which demonstrates the potential and feasibility of the proposed method for satisfactory monitoring performance. Lastly, it is shown that the classification accuracy can be improved by using shallower networks modified from the VGG-16 network to mitigate the overfitting issue. A classification accuracy of 95.58% is achieved by removing two convolutional layers.

在线过程监控可提高产品质量、提高过程稳定性并降低制造成本。本文介绍了使用时频表示和深度神经网络实现实时智能制造过程监控的研究。在机器车削操作期间获得声发射 (AE) 信号，并将其转换为时频 (TF) 表示（图像）格式。然后将深度神经网络应用于图像，根据操作的主轴速度、进给率和切削深度进行过程分类。信号是不稳定的，频率内容随时间变化。讨论和比较了四种常用的时频分析方法，如短时傅立叶变换（STFT）、连续小波变换（CWT）、希尔伯特-黄变换（HHT）、和 Wigner-Ville 分布 (WVD)。研究发现，CWT 技术的多分辨率能力使其能够呈现更准确和更丰富的信号细节。通过传统监控技术对机器车削操作过程进行分类具有挑战性。首先，没有理论直接将信号特征与物理过程相关联。此外，与过程物理相关的重要信号分量被车床发出的大量噪音所掩盖。这些导致难以选择要提取的特征以及获取这些特征的可靠数据。这些障碍被能够以自动方式学习和提取有意义的特征的深度神经网络克服。本研究采用迁移学习方法，使用高性能、之前为图像分类和识别开发的代表性深度神经网络，包括 ShuffleNet、GoogLeNet、ResNet18、ResNet50、VGG16 和 DenseNet201。它们应用于在机器车削操作期间获得的 TF 图像以进行过程分类。VGG16 网络在应用于 12 个类别的过程时产生最高的分类准确率，达到 92.67%，这证明了所提出的方法具有令人满意的监控性能的潜力和可行性。最后，结果表明，通过使用从 VGG-16 网络修改而来的较浅的网络来减轻过拟合问题，可以提高分类精度。通过去除两个卷积层实现了 95.58% 的分类准确率。包括 ShuffleNet、GoogLeNet、ResNet18、ResNet50、VGG16 和 DenseNet201。它们应用于在机器车削操作期间获得的 TF 图像以进行过程分类。VGG16 网络在应用于 12 个类别的过程时产生最高的分类准确率，达到 92.67%，这证明了所提出的方法具有令人满意的监控性能的潜力和可行性。最后，结果表明，通过使用从 VGG-16 网络修改而来的较浅的网络来减轻过拟合问题，可以提高分类精度。通过去除两个卷积层实现了 95.58% 的分类准确率。包括 ShuffleNet、GoogLeNet、ResNet18、ResNet50、VGG16 和 DenseNet201。它们应用于在机器车削操作期间获得的 TF 图像以进行过程分类。VGG16 网络在应用于 12 个类别的过程时产生了最高的分类准确率，达到 92.67%，这证明了所提出的方法具有令人满意的监测性能的潜力和可行性。最后，结果表明，通过使用从 VGG-16 网络修改而来的较浅的网络来减轻过拟合问题，可以提高分类精度。通过去除两个卷积层实现了 95.58% 的分类准确率。VGG16 网络在应用于 12 个类别的过程时产生了最高的分类准确率，达到 92.67%，这证明了所提出的方法具有令人满意的监控性能的潜力和可行性。最后，结果表明，通过使用从 VGG-16 网络修改而来的较浅的网络来减轻过拟合问题，可以提高分类精度。通过去除两个卷积层实现了 95.58% 的分类准确率。VGG16 网络在应用于 12 个类别的过程时产生最高的分类准确率，达到 92.67%，这证明了所提出的方法具有令人满意的监控性能的潜力和可行性。最后，结果表明，通过使用从 VGG-16 网络修改而来的较浅的网络来减轻过拟合问题，可以提高分类精度。通过去除两个卷积层实现了 95.58% 的分类准确率。