Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

According to the change in contact position between the drill edge and the workpiece, drilling machining is classified into three stages, namely, drilling guide, drilling, and drilling out [1]. In the monitoring of drilling, the signal features corresponding to the previous stage are extracted, and the mapping model is established to monitor the drilling process [2]. This can lay a theoretical foundation for realizing highprecision drilling quality analysis; the premise is how to achieve feature enhancement by implementing signal de-noising effectively in a complicated drilling environment. As it is an advanced sensor-and-signal processing technology, a growing number of scholars have been extensively adopting various kinds of sensors to ascertain the drilling process and drilling quality. The monitoring and prediction of tool wear and breakage in drilling are mainly done indirectly through thrust force [3] to [5]. Ferreiro et al. [6] and [7] and Peña et al. [8] completed the burr monitoring by extracting the features from the spindle torque signal in the drilling process. Ramirez et al. [9] established a temperature model for the drilling tool and combined the cutting force signal and temperature signal characteristics to evaluate the surface quality of the drilled surface. Xiao et al. [10] via constructing a valuable indicator, i.e., the wavelet energy ratio around the natural frequency of boring bar vibration signal to monitor tool wear and surface finish quality for deep hole boring, developed a method to monitor and evaluate tool wear during drilling through the monitoring of vibration and acoustic emission signals [11] and [12]. It is well known that the key to achieving the quality monitoring of drilling is to extract abnormal features from the monitoring signals, but the signal features representing drilling quality are often very weak, so it is necessary to pre-process the signal to intensify its features. The above researches on abnormal state monitoring and diagnosis during the machining process can be divided into two classifications: extracting the evident features of monitoring signals to determine abnormal tool damage and drilling quality, and ascertaining the tool wear and the quality of drilling trends by anatomizing the overall monitoring signal. The results of these studies have good guidance significance to ensure high-precision drilling quality. However, they cannot predict or inform when and where tool breakage and quality is abnormal. Therefore, it is of great necessity to ascertain the feature extraction problem of the drilling process signal, establish a mapping model of the monitoring signal and the drilling process, and accurately identify the time and location of abnormal Feature Enhancement Method for Drilling Vibration Signals by Using Wavelet Packet Multi-band Spectral Subtraction Zhou, Y. – Li, Y. – Liu, H. Youhang Zhou1,2,* – Yong Li1 – Hanjiang Liu1 1Xiangtan University, School of Mechanical Engineering, China 2Xiangtan University, Engineering Research Center of Complex Tracks Processing Technology and Equipment of Ministry of Education, China