论文:2013,Vol:31,Issue(2):272-276
引用本文:
王元生, 任兴民, 邓旺群, 杨永锋. 基于经验模态分解的旋转机械故障信号去噪源分离[J]. 西北工业大学
Wang Yuansheng, Ren Xingmin, Yang Yongfeng, Deng Wangqun. An Efficient Denoising Source Separation (DSS) of Rotating Machine Fault Signals Based on Empirical Mode Decomposition (EMD)[J]. Northwestern polytechnical university

基于经验模态分解的旋转机械故障信号去噪源分离
王元生1, 任兴民1, 邓旺群1, 杨永锋2
1. 西北工业大学 振动工程研究所, 陕西 西安 710072;
2. 中国航空动力机械研究所, 湖南 株洲 412002
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。
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   
An Efficient Denoising Source Separation (DSS) of Rotating Machine Fault Signals Based on Empirical Mode Decomposition (EMD)
Wang Yuansheng1, Ren Xingmin1, Yang Yongfeng1, Deng Wangqun2
1. Department of Engineering mechanics, Northwestern Polytechnical University, Xi'an 710072, China;
2. China Aviation Dynamical Machinery Research Institute, Zhuzhou 412002, China
Abstract:
Combining the features of EMD,principal component analysis (PCA) and DSS,we propose an under-determined DSS method based on EMD and PCA,which we believe is efficient. This method is used to deal withthe blind source separation (BSS) problem of rotating machinery in the case of the number of observed mixtures be-ing 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. Thenthe PCA is used to estimate the number of the types of observed signals,and the mixed sources are separated byDSS 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 throughthe 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 importantengineering significance for condition monitoring and fault detection of rotating machines.
Key words:    blind source separation    diagnosis    experiments    fault detection    modal analysis    principal componentanalysis    rotating machinery    signal processing;denoising source separation(DSS)    empirical modedecomposition(EMD)   
收稿日期: 2012-04-15     修回日期:
DOI:
基金项目: 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
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作者简介: 王元生(1983-),西北工业大学博士研究生,主要从事信号处理的研究。
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