机械科学与技术2024，Vol.43Issue(3)：504-512,9.DOI:10.13433/j.cnki.1003-8728.20220270
贝叶斯优化TQWT参数在轴承故障诊断中的应用
Applying Bayesian Optimization of Parameters of Tunable Quality-Factor Wavelet Transform to Bearing Fault
摘要
Abstract
It is costly to use the grid search and optimization algorithm to tune the parameters of tunable quality-factor wavelet transform(TQWT).A method for bearing fault diagnosis based on the Bayesian optimization of TQWT parameters was proposed.The optimal solution of the entropy-kurtosis synthetic objective function was solved by using the Bayesian optimization algorithm in the space of TQWT parameters,according to which the TQWT parameters were set to decompose the original bearing fault signals.The sub-band signal with the minimum value of the entropy-kurtosis index was selected to reconstruct its feature signals with the inverse TQWT transform,and the signal was then processed with an envelope demodulation algorithm.The type of bearing fault was judged with the reconstructed feature signal envelope spectrum.The simulation results on the actually measured bearing vibration signals and their analysis show that the proposed method can accurately extract the characteristic frequency information on fault and diagnose bearing faults at an early stage.关键词
贝叶斯优化/TQWT/熵-峭指标/故障诊断Key words
bayesian optimization/TQWT/entropy-kurtosis index/fault diagnosis分类
机械制造引用本文复制引用
张乐,彭先龙,朱华双..贝叶斯优化TQWT参数在轴承故障诊断中的应用[J].机械科学与技术,2024,43(3):504-512,9.基金项目
陕西省自然科学基础研究计划(2020JM-521) （2020JM-521）
