Tool condition monitoring using I-kaz enhanced kernel extreme learning machine

Abstract In order to improve the accuracy of tool condition monitoring (TCM) in milling, an I-kaz enhanced Kernel Extreme learning Machine (I-kaz_KELM) method is proposed by combining the KELM and I-kaz statistical algorithm. It uses the I-kaz angle function to replace the conventional kernel function to avoid the selection of kernel function and the pre-set of its parameters. A two-layer network model of the I-kaz_KELM is constructed to improve the KELM in feature learning of complex non-linear high-dimensional. Research and analysis of two milling TCM public benchmark datasets (PHM 2010 TCM dataset and NASA TCM dataset) confirmed that the monitoring accuracy of the proposed method is better than SVM and KELM under limited samples. The RESM of the proposed method is reduced by at least 10% compared to the other two methods, and the RESM fluctuation amplitudes of the proposed method with model parameters is reduced by 35%–95% than that of the two other methods. It can be demonstrated that the proposed method can significantly improve learning performance without significantly affecting the learning speed, and has stronger robustness due to reduced sensitivity to the model parameter to be optimized.