Sensors, Vol. 20, Pages 6113: Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning

Sensors, Vol. 20, Pages 6113: Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning Sensors doi: 10.3390/s20216113 Authors: Jun Yuan Libing Liu Zeqing Yang Yanrui Zhang Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research