Deep learning methods have attracted considerable attention in the field of chatter detection due to their exceptional feature extraction and identification capabilities. However, it is challenging to adapt these methods to diverse machining conditions with limited sensor resources. In addition, the computational efficiency of these methods is compromised by their complexity, resulting in a reduced real-time performance for online chatter detection. To address them, this paper proposes a novel deep learning-based framework that combines an efficient signal pre-processing method with a novel lightweight parallel convolutional neural network (LPNN). A fast continuous wavelet transform algorithm is applied to the raw signal, effectively generating time–frequency maps that represent chatter characteristics. The LPNN incorporates the condensed block as a feature extraction unit, which is constructed using depthwise separable convolution. The introduction of the condensed block and the parallel structure increases the depth and width of the designed model while maintaining fewer parameters and higher computational efficiency. A transfer learning strategy is adopted to train the model, reducing the training time and data requirements for online chatter detection. Experimental results using large open-source turning datasets demonstrate the effectiveness of the proposed method in identifying chatter under various machining conditions. The proposed method outperforms other methods with a classification accuracy of 96.5% on the test set. Furthermore, the performance of the proposed method for online chatter detection is validated in our milling experiments, exhibiting enhanced real-time performance compared to other methods.

中文翻译：

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通过轻量级深度学习框架和高效信号预处理进行在线颤振检测


深度学习方法因其卓越的特征提取和识别能力而在颤振检测领域引起了广泛关注。然而，在传感器资源有限的情况下，使这些方法适应不同的加工条件具有挑战性。此外，这些方法的计算效率因其复杂性而受到影响，导致在线颤振检测的实时性能降低。为了解决这些问题，本文提出了一种新颖的基于深度学习的框架，该框架将有效的信号预处理方法与新颖的轻量级并行卷积神经网络（LPNN）相结合。将快速连续小波变换算法应用于原始信号，有效地生成代表颤振特征的时频图。 LPNN 将压缩块作为特征提取单元，使用深度可分离卷积构建。压缩块和并行结构的引入增加了设计模型的深度和宽度，同时保持更少的参数和更高的计算效率。采用迁移学习策略来训练模型，减少了在线颤振检测的训练时间和数据要求。使用大型开源车削数据集的实验结果证明了所提出的方法在识别各种加工条件下的颤振方面的有效性。该方法在测试集上的分类准确率为 96.5%，优于其他方法。此外，所提出的在线颤振检测方法的性能在我们的铣削实验中得到了验证，与其他方法相比，表现出增强的实时性能。