發(fā)布時間：2018-02-04 16:59

  本文關(guān)鍵詞： 風(fēng)電機(jī)組 故障診斷 黎曼流形 協(xié)方差矩陣 流形熵　出處：《哈爾濱工業(yè)大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：由于能源危機(jī)和環(huán)境問題日益嚴(yán)重,風(fēng)能作為綠色無污染能源已受到世界各國的關(guān)注和重視。隨著裝機(jī)規(guī)模的不斷擴(kuò)大,及時發(fā)現(xiàn)風(fēng)電機(jī)組故障并進(jìn)行維修變得越來越重要。齒輪箱是風(fēng)電機(jī)組中故障率較高的部件,振動信號分析是齒輪箱故障診斷中最常用的技術(shù)。風(fēng)電機(jī)組齒輪箱振動信號成分復(fù)雜,具有較強(qiáng)的背景噪聲和明顯的非平穩(wěn)性。研究可以適應(yīng)振動信號非平穩(wěn)性、非高斯特征的新方法是提高其故障診斷技術(shù)水平的關(guān)鍵。因此,開展對風(fēng)電機(jī)組齒輪箱振動信號的特征提取、故障檢測與故障分類等方法研究,對于保證風(fēng)電機(jī)組正常安全運(yùn)行具有重大的實(shí)際意義。本文在對風(fēng)電機(jī)組齒輪箱進(jìn)行振動測試的基礎(chǔ)上,針對傳統(tǒng)振動信號分析方法往往僅對單通道信號分別提取,無法提取多通道振動信號各通道間關(guān)聯(lián)和結(jié)構(gòu)信息的問題,提出了一種基于協(xié)方差矩陣流形分析的齒輪箱故障診斷方法。主要研究內(nèi)容如下:首先,綜述了風(fēng)電機(jī)組齒輪箱故障診斷的基本理論和方法,闡述了風(fēng)電機(jī)組的基本組成,分析了國內(nèi)外研究現(xiàn)狀,介紹了常用的振動信號特征提取方法,在分析多通道振動信號的基礎(chǔ)上,給出了基于多通道振動信號的協(xié)方差矩陣流形表示方法。其次,針對風(fēng)電機(jī)組齒輪箱多通道振動信號的關(guān)聯(lián)和結(jié)構(gòu)特征提取問題,提出了一種基于協(xié)方差矩陣流形表示的橢球可視化故障診斷方法。該方法將多通道振動時間序列轉(zhuǎn)化為一個協(xié)方差矩陣序列進(jìn)行處理,可以有效表達(dá)各通道間的關(guān)聯(lián)結(jié)構(gòu)信息。對協(xié)方差矩陣通過奇異值分解轉(zhuǎn)化為橢球和四元數(shù)表示,可以實(shí)現(xiàn)齒輪箱振動狀態(tài)的可視化,然后進(jìn)行各向異性分析,從而有利于理解振動信號特點(diǎn)并進(jìn)行故障診斷。再次,在多通道振動信號協(xié)方差矩陣流形表示的基礎(chǔ)上,針對齒輪箱振動信號非平穩(wěn)性、非高斯的特點(diǎn),提出了一種基于協(xié)方差矩陣流形黎曼距離的故障檢測與定位方法。該方法用協(xié)方差矩陣流形作為描述子,以黎曼距離作為相似性度量,結(jié)合統(tǒng)計(jì)過程控制圖來實(shí)現(xiàn)對齒輪箱故障的檢測與定位。通過實(shí)驗(yàn)測試與分析,該方法不僅可以有效檢測多通道振動信號間的相關(guān)性,而且在故障檢測準(zhǔn)確率和算法復(fù)雜度上均具有一定的性能優(yōu)勢。最后,針對風(fēng)電機(jī)組齒輪箱故障分類問題,基于振動信號的協(xié)方差矩陣流形表示和傳統(tǒng)的多元多尺度熵算法,提出了一種多尺度流形熵故障分類方法。該方法將傳統(tǒng)的多元多尺度熵推廣到協(xié)方差矩陣上的多尺度流形熵,解決了傳統(tǒng)方法計(jì)算量大、難以量化各通道間相關(guān)性的問題,實(shí)現(xiàn)了齒輪箱振動信號的復(fù)雜性和相關(guān)性特征的有效提取。在故障數(shù)據(jù)上進(jìn)行驗(yàn)證,結(jié)果表明該方法可以有效地降低計(jì)算復(fù)雜度,同時避免了傳統(tǒng)時頻分析中高頻信號的干擾,具有較高的故障分類識別率。
[Abstract]:As a result of energy crisis and environmental problems, wind energy as a green non-pollution energy has been paid attention to by many countries all over the world. With the expansion of the scale of installation, wind energy has been paid more and more attention. It is becoming more and more important to find wind turbine faults and maintain them in time. The gearbox is the component with high failure rate in wind turbine. Vibration signal analysis is the most commonly used technology in gear box fault diagnosis. It has strong background noise and obvious non-stationarity. The study can adapt to the non-stationary vibration signal. The new method of non-#china_person0# feature is the key to improve the technical level of fault diagnosis. The methods of feature extraction, fault detection and fault classification of wind turbine gearbox vibration signal are carried out. It is of great practical significance to ensure the normal and safe operation of wind turbine units. This paper is based on the vibration test of wind turbine gearbox. To solve the problem that the traditional vibration signal analysis method usually only extracts the single channel signal separately, it can not extract the correlation and structure information among the multi-channel vibration signals. A method of gearbox fault diagnosis based on covariance matrix manifold analysis is proposed. The main research contents are as follows: firstly, the basic theory and method of gearbox fault diagnosis for wind turbine are summarized. This paper expounds the basic composition of wind turbine, analyzes the present research situation at home and abroad, and introduces the commonly used vibration signal feature extraction methods, on the basis of analyzing the multi-channel vibration signal. The covariance matrix manifold representation method based on multi-channel vibration signal is presented. Secondly, the correlation of multi-channel vibration signal and the extraction of structural features of wind turbine gearbox are discussed. An ellipsoidal visual fault diagnosis method based on covariance matrix manifold representation is proposed, which transforms the multi-channel vibration time series into a covariance matrix sequence for processing. The covariance matrix can be transformed into ellipsoid and quaternion representation by singular value decomposition, which can visualize the vibration state of the gearbox and then analyze the anisotropy. This is helpful to understand the characteristics of vibration signal and fault diagnosis. Thirdly, on the basis of multi-channel vibration signal covariance matrix manifold representation, aiming at the non-stationary vibration signal of the gearbox, non-#china_person0# characteristics. A fault detection and location method based on Riemannian distance of covariance matrix manifold is proposed, in which covariance matrix manifold is used as descriptor and Riemannian distance as similarity measure. Combined with the statistical process control chart, the gearbox fault detection and location can be realized. Through the experimental test and analysis, the method can not only effectively detect the correlation between the multi-channel vibration signals. And in fault detection accuracy and algorithm complexity has certain performance advantages. Finally, for wind turbine gearbox fault classification problem. Covariance matrix manifold representation based on vibration signal and traditional multi-scale entropy algorithm. A multi-scale manifold entropy fault classification method is proposed, which extends the traditional multi-scale entropy to the multi-scale manifold entropy on the covariance matrix. It is difficult to quantify the correlation between the channels, which can effectively extract the complexity and correlation characteristics of the gearbox vibration signal, and verify the fault data. The results show that this method can effectively reduce the computational complexity and avoid the interference of high frequency signals in the traditional time-frequency analysis. It has a high fault classification and recognition rate.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李培玉;鄭俊;;基于多通道振動信號的港機(jī)車輪軸承故障診斷[J];軸承;2007年09期

2 馮健;曹建剛;;基于Hilbert變換和小波包能量分析的轉(zhuǎn)子斷條故障診斷[J];東北大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年12期

3 蔚婧;廖桂生;曾操;;基于協(xié)方差矩陣特征分解的多通道SAR-GMTI方法及性能分析[J];電子與信息學(xué)報(bào);2009年02期

4 郝井華,劉民,吳澄,陳少卿;一種基于LLM的高維時間序列數(shù)據(jù)異常檢測方法[J];控制工程;2005年03期

5 李廣偉;劉云鵬;尹健;史澤林;;基于黎曼流形的平面目標(biāo)識別[J];自動化學(xué)報(bào);2010年04期

6 徐玲;楊丹;洪明堅(jiān);張小洪;黃劍;;基于平面曲線協(xié)方差矩陣行列式的角點(diǎn)檢測的研究[J];儀器儀表學(xué)報(bào);2009年01期

7 蘇永生;王永生;段向陽;;一種基于改進(jìn)的傅里葉變換的弱故障信號特征提取新方法研究[J];振動與沖擊;2009年08期

8 Zhipeng Liu;Dechang Pi;Jinfeng Jiang;;Density-based trajectory outlier detection algorithm[J];Journal of Systems Engineering and Electronics;2013年02期

9 陳法法;湯寶平;蘇祖強(qiáng);;基于局部切空間排列與MSVM的齒輪箱故障診斷[J];振動與沖擊;2013年05期

10 鄭近德;程軍圣;胡思宇;;多尺度熵在轉(zhuǎn)子故障診斷中的應(yīng)用[J];振動.測試與診斷;2013年02期

相關(guān)博士學(xué)位論文 前1條

1 郭洪澈;兆瓦級風(fēng)力發(fā)電機(jī)組變槳距系統(tǒng)控制技術(shù)研究[D];沈陽工業(yè)大學(xué);2008年

相關(guān)碩士學(xué)位論文 前1條

1 張亮;風(fēng)力發(fā)電機(jī)組齒輪箱早期故障診斷方法研究[D];大連理工大學(xué);2010年

，

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