摘要
为根据管路振动信号准确识别故障类型,提出一种多尺度特征组合优化的航空液压管路故障诊断方法。利用能量比值法确定变分模态分解参数,实现管路振动信号的优化分解,选取最佳模态分量信号进行重构,重构后的信号作为分析信号。选择重构信号的优化多尺度散布熵作为特征指标,构建具有代表性的特征向量集并输入到利用麻雀搜索算法优化的极限学习机网络进行训练,以实现航空液压管路的故障诊断。结果表明:利用所提方法能够准确识别航空液压管路故障类型,为区分航空液压管路故障提供了一种可行的思路。
In order to accurately identify fault types from pipeline vibration signals,a multi-scale feature combination optimization method for aviation hydraulic pipeline fault diagnosis was proposed.The variational modal decomposition parameters were determined by using the energy ratio method to achieve the optimal decomposition of pipeline vibration signals,and the optimal modal component signals were selected for reconstruction as analysis signals.The optimized multi-scale dispersion entropy of the reconstructed signal was chosen as the characteristic indexes,a representative set of eigenvectors was constructed and input to the limit learning machine network optimized by using the sparrow search algorithm for training to realize the fault diagnosis of the aviation hydraulic pipelines.The results show that by using this method,the fault types of aviation hydraulic pipelines can be effectively and accurately identified,which provides a feasible diagnosis idea for distinguishing the fault types of aviation hydraulic pipelines.
作者
薛政坤
汪曦
于晓光
王宠
刘思远
张景博
XUE Zhengkun;WANG Xi;YU Xiaoguang;WANG Chong;LIU Siyuan;ZHANG Jingbo(School of Mechanical Engineering and Automation,University of Science and Technology Liaoning,Anshan Liaoning 114051,China)
出处
《机床与液压》
北大核心
2022年第18期146-152,共7页
Machine Tool & Hydraulics
基金
国家自然科学基金面上项目(51775257)。
关键词
变分模态分解
优化多尺度散布熵
航空液压管路
故障诊断
Variational modal decomposition
Optimized multi-scale dispersion entropy
Aviation hydraulic pipelines
Fault diagnosis
