Time Frequency Monitoring Method of Rotating Machinery Equipment Fault Based on WSN
Due to the complex structure and vibration source of rotating machinery equipment,the threshold set by single fault experience cannot accurately decompose multi-modal faults.In order to im-prove the fault monitoring effect,a time-frequency monitoring method for rotating machinery equipment faults based on WSN is proposed.The time-frequency signal of fault is decomposed by the collective em-pirical mode,the vibration signal at different times is decomposed,the energy of the IMF component is cal-culated,and the normalized energy index and the IMF matrix singular spectrum entropy index are com-bined to complete the decomposition of the fault time-frequency signal of rotating machinery equipment.According to the results of feature decomposition,the trained immune RBF neural network is used to mo-nitor the faults of rotating machinery.The experimental results show that this method can shorten the mo-nitoring time and improve the fault monitoring accuracy.
set empirical moderotating mechanical equipmentfault monitoringtime frequency moni-toringprincipal component analysisRBF neural network
国家科技期刊平台
NSTL
万方数据
