發(fā)布時間：2018-04-19 20:51

  本文選題：雙樹復(fù)小波包變換 + .維譜��； 參考：《河南理工大學(xué)學(xué)報(自然科學(xué)版)》2016年06期

【摘要】：針對滾動軸承故障識別困難這一問題,提出了基于雙樹復(fù)小波包變換和1.5維譜的診斷方法。首先通過雙樹復(fù)小波包變換將復(fù)雜的、非平穩(wěn)的原始故障信號分解為若干個不同子帶信號分量,繼而利用峭度評價指標(biāo)從分解所得結(jié)果中篩選出蘊(yùn)含豐富特征信息的子帶信號分量,將其視為最佳分量并做進(jìn)一步包絡(luò)解調(diào)運(yùn)算,最后計算所得包絡(luò)信號的1.5維譜,從中提取出軸承故障特征信息。實測信號分析結(jié)果表明,基于雙樹復(fù)小波包變換和1.5維譜的診斷方法能夠?qū)崿F(xiàn)滾動軸承故障類型的有效判定,具有一定工程應(yīng)用價值。
[Abstract]:Aiming at the difficulty of fault identification of rolling bearing, a diagnosis method based on double tree complex wavelet packet transform and 1.5 dimension spectrum is proposed. Firstly, the complex and non-stationary original fault signals are decomposed into several different sub-band signal components by the double tree complex wavelet packet transform. Then the kurtosis evaluation index is used to select the subband signal component which contains rich characteristic information from the decomposition result, which is regarded as the best component and further envelope demodulation operation. Finally, the 1.5 dimension spectrum of the envelope signal is calculated. The bearing fault feature information is extracted from it. The analysis results of measured signals show that the diagnosis method based on double tree complex wavelet packet transform and 1.5 dimensional spectrum can effectively judge the fault type of rolling bearing and has certain engineering application value.
【作者單位】： 河北金融學(xué)院信息管理與工程系;燕山大學(xué)經(jīng)濟(jì)管理學(xué)院;
【基金】：河北省自然科學(xué)基金資助項目(E2015502056)
【分類號】：TH133.33

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