Tool Wear State Monitoring Based on Variational Mode Decomposition and Correlation Dimension and Relevance Vector Machine
HE Zhi-jian,ZHOU Zhi-xiong,HUANG Xiang-ming
1. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, Hunan 410082,China
2. Hunan College of Information, Changsha, Hunan 410082,China
Abstract：According to unstable-state and non-linear characteristics of tool wear signal, a recognition method based on variational mode decomposition correlation dimension and relevance vector machine is proposed. First, the acoustic emission signal was decomposed by variational mode decomposition, then a series of components were obtained. The components generated by variational mode decomposition have different sensitivity to condition of tool wear. According to the mutual information of components, sensitive components were selected, which were used to calculate the correlation dimension of sensitive components and combined into a feature vector. Finally, the feature vector were input relevance vector machine, which will be classified, trained and tested, in order to identify the state of tool wear. By comparing classification accurate rate of variational mode decomposition and applied empirical model decomposition methods, the superiority of the proposed method based on variational mode decomposition is demonstrated in state recognition of tool wear.
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2018, Vol. 39 
