摘要
针对传统信号采样方式会产生大量冗余数据的问题,提出一种综合压缩感知和变分模态分解(VMD)方法的船舶推进轴系轴承振动故障分析方法。首先利用观测矩阵对采样信号降维观测,并运用正交匹配追踪(OMP)方法重构信号,其中,重构循环次数由设定阈值或限定次数动态决定。在此基础上,将重构数据利用VMD进行分解,根据峭度准则选取最佳模态分量分析其包络谱,与故障特征频率进行匹配。经数据实验结果验证,论文所述的算法不仅有效地识别了船舶推进轴系轴承的故障特征,而且具有一定的实时性。
Aiming at the problem that the traditional signal sampling method will generate a large amount of redundant data,a ship propulsion shaft bearings fault analysis method based on compressed sensing and variational mode decomposition is proposed.Firstly,the observation matrix is used to reduce the dimensionality of the sampled signal,and the orthogonal matching pursuit(OMP)method is used to reconstruct the signal. The number of reconstruction cycles is dynamically determined by a set threshold or a limited number of times. On this basis,the reconstructed data is decomposed by VMD,and the optimal modal component is selected according to the kurtosis criterion to analyze the envelope spectrum and match the fault characteristic frequency. The experimental results show that the proposed algorithm not only can effectively identify the fault characteristics of ship propulsion shaft bearings,but also has a certain real-time performance.
作者
张涵
万振刚
ZHANG Han;WAN Zhengang(Jiangsu University of Science and Technology,Zhenjiang 212000)
出处
《舰船电子工程》
2020年第1期157-161,共5页
Ship Electronic Engineering
基金
江苏省研究生科研与实践创新计划项目（编号：SJCX17_0603）资助
关键词
压缩感知
OMP
VMD
船舶推进轴系
故障诊断
compressed sensing
OMP
VMD
ship propulsion shaft bearings
fault diagnosis
