Fault analysis and reliability evaluation for motorized spindle of cycloidal gear grinding machine based on multi-source bayes

Abstract

A Monte Carlo simulation method based on multisource bayes is proposed to improve the reliability of motorized spindles in cycloid gear grinding machines and reduce their failure rate. Based on field investigations and motorized spindle maintenance records, a fault tree model of a motorized spindle was established, and the fuzzy importance of each bottom event was evaluated. The fault tree of a motorized spindle was used as a Monte Carlo reliability simulation model, and its importance was used as the input parameter for the simulation. The reliability evaluation index of the motorized spindle was obtained at different simulation times. The feasibility and accuracy of the reliability simulation were verified by comparing the importance and simulation importance. A vibration test was designed for bearing faults with high importance, and fault extraction was performed by combining the wavelet packet transform and empirical mode decomposition. This method can also be used to simulate and analyze the reliability of other equipment or machine tools.