Multi-factor machining condition monitoring method based on ordinal pattern analysis and image matching

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.