Tool wear state recognition based on feature selection method with whitening variational mode decomposition

Abstract

• A signal decomposition method based on WVMD is proposed, which makes the intrinsic mode functions independent of each other and can effectively avoid mode mixing phenomenon. • The strong correlation force signal features from sensitive feature set are selected by proposed JIE method . Redundant features unrelated to tool wear are removed to achieve data dimension reduction. • The classification of tool wear state is realized by OPF algorithm, which can effectively reduce calculation time while ensuring classification accuracy . Tool states affect the surface quality and equipment stop time. It is essential to seek a method with high accuracy and efficiency to model and predict tool states. Cutting process signal is usually used to in industry monitoring, which contains lots of fault information. In this paper, the spectrum analysis of milling force is carried out to obtain the signal frequency that can reflect tool wear degree. A force signal decomposition model based on whitening variational mode decomposition (WVMD) is established to screen out sensitive signals, which effectively avoids mode mixing. Joint information entropy (JIE) is adopted to select the force signal features. Compared with other dimensionality reduction algorithms, the optimal feature subset obtained by JIE method can reflect strong relativity between features and the wear state. The classification is realized by the optimal-path forest (OPF) algorithm. According to the experimental results, compared with other recognition models, WVMD-JIE-OPF method has a classification accuracy as high as 98.41%. The training speed of OPF is increased by 58.43% compared with LSSVM, which shows excellent tool condition monitoring performance.