本文選題：復數(shù)據(jù)經(jīng)驗模態(tài)分解 + 模態(tài)混疊　； 參考：《水力發(fā)電學報》2017年02期

【摘要】：水輪機組是一個復雜的非線性動力系統(tǒng),振動信號往往表現(xiàn)為非平穩(wěn)性、非線性的特點,經(jīng)驗模態(tài)分解是一種新的時域分析方法,具有很好的適應性,較為適合處理非平穩(wěn)性信號,但存在嚴重端點效應、模態(tài)混疊等問題。改進的集成經(jīng)驗模態(tài)分解一定程度上能夠抑制模態(tài)混疊,但也會帶來新的模態(tài)混疊、頻譜丟失、運算量增大等問題。因此,本文將復數(shù)據(jù)經(jīng)驗模態(tài)分解運用到水輪機水導軸承的故障診斷中,添加白噪聲作為虛部,從而構成復信號,通過白噪聲在各個方向的投影來影響極值點的選取,同時利用噪聲投影的影響再求包絡質(zhì)心的時候被消除的特性,從而抑制模態(tài)混疊。并通過水電站的實測信號驗證該方法的有效性。
[Abstract]:The hydraulic turbine unit is a complex nonlinear dynamic system. The vibration signal is usually nonstationary and nonlinear. Empirical mode decomposition is a new time domain analysis method with good adaptability. It is more suitable to deal with non-stationary signals, but there are some problems such as serious endpoint effect, modal aliasing and so on. The improved empirical mode decomposition can restrain the mode aliasing to a certain extent, but it will also bring some new problems such as mode aliasing, spectrum loss and the increase of computation. Therefore, in this paper, the empirical mode decomposition of complex data is applied to the fault diagnosis of hydraulic turbine hydraulic bearing, and the white noise is added as the imaginary part to form the complex signal, and the selection of extreme points is influenced by the projection of white noise in all directions. At the same time, the noise projection is used to calculate the characteristic of the centroid of the envelope, which can suppress the mode aliasing. The validity of the method is verified by the measured signal of the hydropower station.
【作者單位】： 西安理工大學水利水電學院;甘肅省電力科學研究院;
【基金】：國家自然科學基金(51279161) 陜西省水利科技計劃項目(2015slkj-04) 電網(wǎng)公司科技項目(522722150012)
【分類號】：TV738
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本文編號：1955612