Tool Wear State Monitoring Based on Diagonal Slice Spectrum Amplitude Feature

Tool wear is one of the most common tool failure modes. And the vibration signals of tool wear show strong nonlinear and non-Gaussian characteristics. Bispectrum is an effective method to analyze nonlinear and non-Gaussian signals. However, bispectrum is not only difficult to understand, but also computationally intensive. This paper presents a milling tool condition monitoring method based on the amplitude feature of tool characteristic frequency of bispectral diagonal slices. Bispectral diagonal slice is used as tool wear signal processing method to identify tool states. And the amplitude feature of tool characteristic frequency of bispectral diagonal slices was selected to monitor tool wear. The effectiveness of the proposed method has been verified by the experiment. Results showed that the proposed approach is able to effectively extract the feature information of tool wear state, and reduces the calculation cost.