Whirling detection in deep hole drilling process based on multivariate synchrosqueezing transform of orthogonal dual-channel vibration signals. (15th March 2022)

• Record Type: Journal Article
• Authors: Si, Yue; Kong, Lingfei; Chin, Jih-Hua; Guo, Weichao; Wang, Qilong
• Is Part Of: Mechanical systems and signal processing. Volume 167:Part B(2022)
• Publication Date: 2022-03-15
• Subjects: Deep hole machining -- Whirling vibration -- Multivariate synchrosqueezing transform -- Orthogonal dual-channel signals -- On-line monitoring; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108621
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: An novel whirling detection method based on multivariate synchrosqueezing transform is presented for deep-hole drilling process. The multivariate synchrosqueezing transform (MSST) method fused the orthogonal dual-channel spindle vibration signals in deep-hole drilling process. The whirling indicator is constructed based on the singular values of the time-frequency spectrum of MSST. The whirling detection method is validated by BTA deep hole drilling tests. Abstract: In the deep hole drilling process, whirling vibration is easily initiated due to slender drill bar having larger length to diameter with low torsional and bending stiffness, which may increase the roundness error and roughness of the hole, even damage the tool and the wall of hole. Therefore, whirling detection is an important task to improve part quality and productivity in the deep hole drilling process. Due to the background noise and other disturbances, the whirling feature signal contained in the vibration signal is very puny. The whirling detection in deep hole drilling process based on vibration signal has turn into a challenging task. In this paper, a whirling detection method is proposed based on multivariate synchrosqueezing transform (MSST) of orthogonal dual-channel vibration signals. Firstly, the empirical wavelet transform method is used to extract the high multiple frequency components of the spindle rotation frequency. And then, the MSST is introduced to enhance the whirling feature signal contained in the high multiple frequency components. Finally, singular value decomposition (SVD) method is employed to condense the time–frequency spectrum of MSST and the whirling indicator is constructed based on the singular values. The proposed method is validated with BTA deep hole drilling tests, and the results show the superiority of the proposed method and indicate the proposed method has great potential to be used for the online whirling detection.