本文選題：VMD + SVD　； 參考：《石油礦場機械》2016年08期

【摘要】：旋轉(zhuǎn)機械結(jié)構(gòu)復(fù)雜,振動信號信噪比低且多為非平穩(wěn)、非線性的多分量信號,出現(xiàn)故障時難以有效地進行診斷。常規(guī)的小波分析方法需根據(jù)信號特點選取特定的小波基和分解層次,自適應(yīng)分解方法如EMD、EEMD等存在頻率混疊及虛假分量現(xiàn)象,在提取微弱信號時易造成誤判。提出了一種基于變分模態(tài)分解(VMD)和奇異值分解(SVD)的故障診斷方法。首先對信號進行VMD分解,并對分解得到的固有模態(tài)函數(shù)分量進行SVD降噪;然后從降噪后的分量中選取故障特征分量進行時頻域及包絡(luò)譜分析,最終確定故障類型。仿真及試驗結(jié)果表明,該方法可以有效地降低噪聲,提取微弱故障信息,實現(xiàn)故障診斷。
[Abstract]:Rotating machinery has complex structure, low signal-to-noise ratio (SNR) of vibration signals and non-stationary, nonlinear multi-component signals, so it is difficult to diagnose effectively when faults occur. Conventional wavelet analysis methods need to select specific wavelet bases and decomposition levels according to the characteristics of signals. Adaptive decomposition methods such as EMD-EEMD have frequency aliasing and false component phenomena which can easily lead to misjudgment when extracting weak signals. A fault diagnosis method based on variational mode decomposition (VMD) and singular value decomposition (SVD) is proposed. Firstly, the signal is decomposed by VMD, and the natural mode function component is decomposed by SVD, and then the fault characteristic component is selected from the noise reduction component to analyze the time-frequency domain and the envelope spectrum, and finally the fault type is determined. Simulation and experimental results show that this method can effectively reduce noise, extract weak fault information and realize fault diagnosis.
【作者單位】： 中國石油大學(xué)(北京)機械與儲運工程學(xué)院;中國石油新疆油田分公司實驗檢測研究院;中國石油西南管道公司貴陽輸油氣分公司;
【基金】：國家自然科學(xué)基金資助(51504274)
【分類號】：TH17

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