Sun Wei, Zhou Jie, Sun Bintao, Zhou Yuqing, Jiang Yongying
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China.
College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314001, China.
Micromachines (Basel). 2022 May 31;13(6):873. doi: 10.3390/mi13060873.
Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.
刀具状态监测（TCM）对于提高工件的制造效率和表面质量至关重要。数据驱动的机器学习方法在刀具状态监测中被广泛应用，并取得了许多良好的成果。然而，在实际工业场景中，目标域中无法及时获得标记数据，这显著影响了数据驱动方法的性能。为了克服这一问题，提出了一种将马尔可夫转移场（MTF）和深度域自适应网络（DDAN）相结合的新型刀具状态监测方法。通过马尔可夫转移场将刀具状态监测实验中采集到的少量振动信号表示为二维图像，以丰富原始信号的特征。使用迁移的ResNet50来提取这些二维图像的深度特征。采用深度域自适应网络来提取源域和目标域之间的深度域不变特征，其中应用最大均值差异（MMD）来测量两个不同分布之间的距离。刀具状态监测实验表明，所提出的方法明显优于其他三种基准方法，并且在不同工作条件下更具鲁棒性。
