摘要
针对旋转机械故障诊断中信号源不足的问题,综合经验模态分解(EMD)、主成量分析(PCA)和去噪源分离(DSS)各自的优点,提出一种基于EMD和PCA的欠定去噪源分离方法(EMD-PCA-DSS)。首先通过EMD求出本征模函数(IMF),进而重组IMF分量和原观测信号作为新的观测信号,解决了盲源分离(BSS)中源信号数据不足的问题。然后,通过PCA估计观测信号的源数,利用DSS估计出源信号。将该方法应用于某转子的实测故障信号分析中,诊断出转子发生了不平衡故障,表明该方法在旋转机械故障诊断中的有效性,这对于机械设备的状态监测和故障诊断具有重要的工程意义。
Combining the features of EMD, principal component analysis （PCA） and DSS, we propose an underdetermined DSS method based on EMD and PCA, which we believe is efficient. This method is used to deal with the blind source separation （BSS） problem of rotating machinery in the case of the number of observed mixtures being less than that of contributing sources. The observed signals are decomposed into some intrinsic mode functions （IMFs） with the EMD method. These IMFs and original observations were composed into new observations. Then the PCA is used to estimate the number of the types of observed signals, and the mixed sources are separated by DSS algorithm. It is verified that the new method yields a correct estimate of source number in the simulation tests. Applying EMD-PCA-DSS method to the rotor fault detection, we have diagnosed the unbalance phenomenon through the measured fault signals of the rotor. The simulation results, experimental results, and their analysis show prelim- inarily that the EMD-PCA-DSS method is indeed efficient in analyzing the fault diagnosis and it has an important engineering significance for condition monitoring and fault detection of rotating machines.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2013年第2期272-276,共5页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(10902084
11272257)
陕西省自然科学基础研究(2011JQ1011)
航空科学基金(20112108001)
西北工业大学基础研究基金(JC201242)资助
关键词
盲源分离
诊断
试验
故障检测
模型分析
主成量分析
旋转机械
信号处理
去噪源分离
经验模态分解
blind source separation, diagnosis, experiments, analysis, rotating machinery, signal processing
decomposition （ EMD ） fault detection, modal analysis, principal component denoising source separation （DSS）, empirical mode
