Identification Method of Vibration Drilling Bit Wear State Based on Signal Im-aging and Deep Learning

Abstract

In vibration-assisted drilling, the wear state of the drill bit affects the processing quality of the hole. The traditional method of identifying the wear state of the drill bit adopts the method of packet decomposition, ignoring the timing characteristics of the signal. In this paper, the force and acoustic emission signals in vibration-assisted drilling are used. The Gram angle field converts the onedimensional time series into a two-dimensional image, while retaining the trajectory of the time series in the high-dimensional space. Based on the Graham difference field (GADF) image of force and AE, the Inception improved convolutional neural network (IN-CNN) is used to identify the wear state. The experiment proves that compared with the traditional convolutional neural network, BP neural network and support vector machine, the recognition rate of IN-CNN drill wear state based on GADF is 93.1 %, which is increased by 2.5 %, 10.6 % and 8.1 % respectively. It provides a reliable condition monitoring method for the state identification of the drill bit in semi-closed vibration-assisted machining, and has practical engineering significance for improving the machining accuracy and efficiency of composite equal-holes.

Dynamic Analysis of the Propulsion Process of Tunnel Boring Machines

“…Based on the above analysis, future studies can combine the influence of TBM drive system and other factors on the propulsion system, and further optimize the propulsion parameters through actual experimental data. Through the specific propulsion parameters, it is helpful to study the optimal matching relationship between mechanical model and excavation parameters in the process of TBM propulsion, so as to improve the excavation efficiency and stability of TBM, reduce energy consumption and production costs, and improve the economic benefits of industrial production [27].…”