Spindle Status Monitoring and Fault Feature Information Acquisition Based on Rotor Sensing

As one of the core components of CNC machine tools, the performance and status of the spindle have an important impact on the normal operation of the CNC machine tools. However, the spindle is often difficult to monitor its condition due to its complex structure and complex working conditions and failure modes. Therefore, the rotor sensing technology is used to monitor the state of the spindle, and the low-cost, low-power micro-electromechanical system is used to replace the traditional sensor (MEMS), so that the MEMS accelerometer can be directly installed on the rotating shaft, providing more accurate dynamics for the rotating machinery information. This article first discusses the rotor sensing technology, and then takes a certain type of electric spindle as the research object, analyzes its structure, establishes geometric models and dynamic equations, and uses ANSYS software to conduct modal analysis to determine the fault transmission path, And then studied the bearing faults with high failure rate and greater hazard in the electric spindle, and proposed a rolling bearing fault feature extraction method based on complementary set empirical mode decomposition (CEEMD) and modulation signal bispectrum. First, CEEMD is used to decompose several IMF components, and the correlation between each IMF and the original signal is calculated according to the correlation coefficient criterion, and the IMF components that meet the requirements are reconstructed. Finally, the reconstructed signal is decomposed by MSB and the fault characteristic frequency is extracted. Experiments have verified the analysis results of the CEEMD-Fast Spectral Kurtosis (FK) method. The CEEMD-MSB method proposed in this paper can obtain fault characteristics more accurately, which proves the effectiveness and superiority of the proposed method.