Multisensory Data-Driven Health Degradation Monitoring of Machining Tools by Generalized Multiclass Support Vector Machine

Abstract

Health degradation monitoring of machining tools is of great importance in industrial application field. In this paper, a novel multisensory data-driven health degradation monitoring system schema for the machining tools is proposed by using a generalized multiclass support vector machine (GenSVM). In this schema, multidimensional feature extraction is implemented in the time domain, frequency domain, and time-frequency domain based on the time domain statistical analysis, power spectrum analysis, and complete ensemble empirical mode decomposition with adaptive noise, respectively. On the basis, effective features that are strongly associated with the degradation process are picked out using a Pearson correlation coefficient. Meanwhile, a new and flexible GenSVM model is constructed to identify the health state of machining tools, which integrates a simplex encoding and iterative majorization optimization algorithm. A practical application case study is implemented to verify the effectiveness of the proposed method. The results show the superior performance of the proposed method compared with other standard methods.