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Fault analysis and reliability evaluation for motorized spindle of cycloidal gear grinding machine based on multi-source bayes
jie LI 3
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School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, Henan Province, China
State Key Laboratory of Intelligent Mining Heavy Equipment, Luoyang, Henan Province, China
AVIC Jonhon Optronic Technology Co., Ltd., Luoyang, Henan Province, China
These authors had equal contribution to this work
Submission date: 2023-10-18
Final revision date: 2023-10-27
Acceptance date: 2023-11-08
Online publication date: 2023-11-10
Publication date: 2023-11-10
Corresponding author
Huiliang Wang   

Henan University of Science and Technology, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(1):175010
  • A Monte Carlo simulation method based on multi-source Bayes is proposed to establish the fuzzy fault tree model of motorized spindles.
  • Using a fault tree as the simulation model, the feasibility of reliability simulation is verified by comparing fuzzy importance with simulation importance.
  • The wavelet packet transform is combined with empirical mode decomposition to extract faults and simulate equipment reliability.
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.
This work was supported in part by the Provincial Science and Technology Research and Development Program Joint Fund under Grant 222103810040, in part by the 2023 Key scientific research of universities in Henan Province Project, under Grant 23A460017
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