Sensitive Features Extraction of Wear Monitoring Signals Based on Wavelet Packet Energy Spectrum

Abstract

Tool condition monitoring is an essential issue in manufacturing process quality improvement, and there exist numerous sources of tool condition information. Force signals, vibration signals and acoustic emission signals are widely considered to be effective for identifying tool wear conditions, but the dilemma of redundant information is still hardly avoided. Therefore, to extract effective information of tool wear, this paper proposes a method to identify sensitive frequency band in the milling process based on wavelet packet energy spectrum. First, wavelet packet is proposed to decompose the vibration signal into multiple frequency bands. In addition, wavelet singular entropy is proposed to select appropriate decomposition parameters as well, so that weak vibration signals can be effectively extracted. Subsequently, the energy information is obtained from the decomposed frequency bands as characteristic parameters. Then identify the frequency bands sensitive to tool wear with Pearson correlation analysis. Finally, PHM2010 datasets are used to verify the feasibility and effectiveness of the proposed method, and the results demonstrate the applicability of the proposed method in practice for sensitive frequency band identification of tool wear.