Abnormal Detection Method of Tool Machining Monitoring Data Based on Self-Paced Learning

Abstract

Aimed at the problem that the accuracy of tool remaining life prediction was reduced due to the abnormal monitoring data in machining process, a data anomaly detection method based on self-paced learning was proposed. First, a multi-layer perceptron model was established to correlate the tool processing monitoring data with the tool remaining life. Next, in the process of updating model weight, the model weight parameters were fixed first, and the loss threshold of abnormal samples was obtained by predicting loss fitting Gaussian distribution. Then, the loss function based on the self-paced learning method was constructed to update model parameters iteratively. At the end of the model training, abnormal samples were divided according to the loss threshold. Finally, the actual processing monitoring data of turbine rotor groove were used to verify the proposed method, and compare with the local anomaly factor algorithm, the density-based clustering algorithm, the K-means algorithm, the isolated forest algorithm, and the one-class support vector machines.

Keywords

• tool machining monitoring
• data quality
• anomaly detection
• self-paced learning