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Aiming at the skew and misalignment between the inner and outer rings of the spindle bearing during the installation process, a new method for detecting the spindle bearing assembly state is proposed combined an improved maximum correlated kurtosis deconvolution (IMCKD) and support vector machine (SVM). The vibration and impulse signals induced by bearing assembly skew or dislocation are quite weak. In this paper, the collected vibration signals were preprocessed by IMCKD algorithm with optimized parameters between the normal and skewed state of the outer rings at first, which aims to deconvolve periodic impulse characteristic and enhance the periodic impulse component from a new assembly spindle vibration signal. Then, wavelet packet decomposition method was adopted to extract the characteristic energy value of different frequency bands, and the eigenvector of the assembly state of the spindle bearing was calculated. Furthermore, the support vector machine classification method is implemented to analyze the eigenvectors under different assembly states. Finally, the proposed method was verified by experimental results. The results show that the IMCKD and SVM methods can effectively detect the bearing assembly skew state of the spindle with an accuracy of more than 95 %.

Investigation assembly state of spindle bearing based on improved maximum correlated kurtosis deconvolution and support vector machine