基于小波包分析和随机森林算法的滚动轴承故障诊断方法OA

To quickly analyze the types of faults in rolling bearings and reduce the impact of these faults on the functionality of mechanical equipment,based on a fault diagnosis process using vibration signals,a research method for diagnosing faults in rolling bearings is proposed,using the random forest algorithm and wavelet packet analysis theory.Firstly,the bearing vibration signal is projected onto the wavelet packet basis to obtain a series of significantly different coefficients,which characterise the features of the bearing vibration signal.Secondly,the statistical sampling Bagging algorithm is employed to extract N small sample datasets from a large sample feature set,generating N decision trees.Each decision tree is imagined as an expert in various fields,allowing them to make decisions through a voting process.Finally,the decision results of all decision trees are aggregated,and the decision result with the most votes is taken as the final output of the algorithm.Research shows that the method can accurately,effectively,and precisely distinguish the types of bearing faults,providing new insights for bearing fault diagnosis methods.