TY - GEN
T1 - Multi-factor machining condition monitoring method based on ordinal pattern analysis and image matching
AU - Li, Yazhou
AU - Dai, Wei
AU - Li, Tong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The existing machining process condition monitoring methods usually only monitor the single anomaly, ignoring the multi-factor coupling anomaly in the actual complex machining process. Aiming at three kinds of typical anomalies frequently occurring in cutting, a new multi-factor coupling machining condition monitoring method based on ordinal pattern (OP) analysis and image matching is proposed. Firstly, the OP analysis model is developed to transform the condition monitoring signal into a gray image based on multi-parameter ordinal pattern spectrum (OPS), which optimizes the parameter selection process. Then, an OPS image dictionary template set of different condition monitoring signals is established. A condition recognition method based on OPS image matching is proposed to identify the sample processing state. Finally, a cutting experiment with 8 machining states is designed to verify the effectiveness of the method. The results show that the proposed method can accurately identify various cutting anomalies in different machining environments.
AB - The existing machining process condition monitoring methods usually only monitor the single anomaly, ignoring the multi-factor coupling anomaly in the actual complex machining process. Aiming at three kinds of typical anomalies frequently occurring in cutting, a new multi-factor coupling machining condition monitoring method based on ordinal pattern (OP) analysis and image matching is proposed. Firstly, the OP analysis model is developed to transform the condition monitoring signal into a gray image based on multi-parameter ordinal pattern spectrum (OPS), which optimizes the parameter selection process. Then, an OPS image dictionary template set of different condition monitoring signals is established. A condition recognition method based on OPS image matching is proposed to identify the sample processing state. Finally, a cutting experiment with 8 machining states is designed to verify the effectiveness of the method. The results show that the proposed method can accurately identify various cutting anomalies in different machining environments.
KW - complex cutting process
KW - condition monitoring
KW - image matching
KW - multi-factor coupling anomaly
KW - ordinal pattern analysis
UR - https://www.scopus.com/pages/publications/85143144685
U2 - 10.1109/PHM-Yantai55411.2022.9941748
DO - 10.1109/PHM-Yantai55411.2022.9941748
M3 - 会议稿件
AN - SCOPUS:85143144685
T3 - 2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022
BT - 2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022
A2 - Guo, Wei
A2 - Li, Steven
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022
Y2 - 13 October 2022 through 16 October 2022
ER -