基于WSN的旋转机械设备故障时频监测方法
Time Frequency Monitoring Method of Rotating Machinery Equipment Fault Based on WSN
孙留存 1胡从川 1钱大龙1
作者信息
- 1. 中国绿发投资集团有限公司,北京 100020
- 折叠
摘要
由于旋转机械设备结构和振源较为复杂,以单一故障经验设置的阈值无法准确分解多模态故障,为提升故障监测效果,提出基于 WSN的旋转机械设备故障时频监测方法.引入集合经验模态分解故障时频信号,分解不同时刻的振动信号,计算IMF分量的能量,结合归一化能量指标和IMF矩阵奇异谱熵指标,完成旋转机械设备故障时频信号分解.根据特征分解结果,运用训练后免疫RBF神经网络监测旋转机械设备故障.实验结果表明,该方法能够缩短监测时间、提高故障监测准确率.
Abstract
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.
关键词
集合经验模态/旋转机械设备/故障监测/时频监测/主成分分析/RBF神经网络Key words
set empirical mode/rotating mechanical equipment/fault monitoring/time frequency moni-toring/principal component analysis/RBF neural network引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据