本文選題：滾動(dòng)軸承 + 非平穩(wěn)分析�。� 參考：《西安電子科技大學(xué)》2014年博士論文

【摘要】：滾動(dòng)軸承是旋轉(zhuǎn)機(jī)械的重要組成部件,其性能狀態(tài)對(duì)機(jī)械設(shè)備運(yùn)行及效率起至關(guān)重要的作用。基于振動(dòng)分析的滾動(dòng)軸承智能故障診斷已成為國(guó)內(nèi)外學(xué)者的研究熱點(diǎn),但相關(guān)研究主要針對(duì)固定工況運(yùn)行環(huán)境,難以滿足工程實(shí)踐中載荷變化和轉(zhuǎn)速波動(dòng)的變工況故障診斷需要。本文基于滾動(dòng)軸承振動(dòng)信號(hào)的非平穩(wěn)、非高斯特性,對(duì)特征參數(shù)提取、特征向量?jī)?yōu)化、變工況故障診斷和性能退化評(píng)估等問題展開研究。主要工作概述如下:1.研究振動(dòng)信號(hào)的連續(xù)小波變換,提出一種基于最小香農(nóng)熵和奇異值分解的Morlet小波參數(shù)優(yōu)化方法。最小香農(nóng)熵意味著小波系數(shù)稀疏,保證小波波形和信號(hào)之間的較高相似度,奇異值分解可以檢測(cè)序列的周期特性,二者相結(jié)合能提取更有效的故障特征信息。研究最優(yōu)Morlet小波系數(shù)的常用統(tǒng)計(jì)參量性質(zhì),標(biāo)準(zhǔn)差、均值、均方根和無窮范數(shù)在不同軸承狀態(tài)下差異性顯著,作為特征參數(shù)獲得可靠的故障診斷結(jié)果。最后用比較實(shí)驗(yàn)證明Morlet小波參數(shù)優(yōu)化的有效性和可靠性。2.研究振動(dòng)信號(hào)的小波包分解,提出一種差異性和相似性相結(jié)合的特征向量?jī)?yōu)化方法。振動(dòng)信號(hào)小波包分解獲得多個(gè)帶寬相同的子頻帶,但有效故障特征信息只分布在少量子帶中,特征向量存在冗余信息。選擇Daub8小波,根據(jù)子帶寬度和諧波頻率估算小波包分解層數(shù),并將子帶能量作為參數(shù)構(gòu)造特征向量�；贔isher線性距離測(cè)度,差異性優(yōu)化選出不同軸承狀態(tài)下距離較大的特征向量行向量,相似性優(yōu)化選出特征向量?jī)?nèi)距離較小的行向量。優(yōu)化特征向量具有較大的類間差異性和類內(nèi)相似性,在突出故障特征信息同時(shí)抑制了干擾成分。比較實(shí)驗(yàn)表明文中優(yōu)化方法的辨識(shí)精度優(yōu)于文獻(xiàn)方法。3.對(duì)振動(dòng)信號(hào)進(jìn)行小波降噪研究,提出一種基于短時(shí)過零率的工況魯棒早期故障診斷方法。過零率只與信號(hào)通過零點(diǎn)的頻度有關(guān)而與波形或幅度無關(guān),對(duì)工況改變導(dǎo)致的振動(dòng)信號(hào)波形變化魯棒,也能在一定程度上表征信號(hào)的頻域信息。確定小波函數(shù)、分解層數(shù)和閾值策略后,研究小波降噪信號(hào)的短時(shí)過零率特點(diǎn),其在不同故障狀態(tài)下的差異性明顯,在故障相同但工況不同時(shí)又具有較大的相似性,是一種工況魯棒的特征參數(shù)。使用任意一種工況的數(shù)據(jù)訓(xùn)練模型,都能正確辨識(shí)當(dāng)前工況和其它三種工況的故障類型,實(shí)現(xiàn)工況魯棒的早期故障診斷。4.研究滾動(dòng)軸承振動(dòng)信號(hào)的非高斯特性,提出一種基于雙譜主成分分析的智能故障診斷方法。先對(duì)振動(dòng)信號(hào)的雙譜特性進(jìn)行研究,其幅度和分布特性在不同故障類型時(shí)具有明顯的差異性,在故障相同但工況不同時(shí)又具有一定的相似性。使用主成分分析方法提取雙譜中的有效特征信息,取其幅值作為特征參數(shù),實(shí)現(xiàn)了不同工況和不同故障程度的軸承狀態(tài)判別。此外,零載荷工況數(shù)據(jù)訓(xùn)練的模型,能辨識(shí)其它三種不同工況的故障類型,具有工況魯棒的故障診斷功能。5.對(duì)滾動(dòng)軸承性能退化評(píng)估進(jìn)行研究,提出一種基于隱馬爾可夫模型距離的性能退化評(píng)估指標(biāo)。先設(shè)計(jì)滾動(dòng)軸承加速度疲勞壽命試驗(yàn),并自制數(shù)據(jù)采集系統(tǒng)記錄6205軸承性能退化過程的振動(dòng)加速度信號(hào)。研究常用診斷指標(biāo)在性能退化過程中的變化規(guī)律,發(fā)現(xiàn)滾動(dòng)軸承性能退化過程經(jīng)歷六個(gè)不同階段,將其命名為:正常狀態(tài)、早期故障、中度故障、嚴(yán)重故障、預(yù)警階段和軸承失效。振動(dòng)信號(hào)的均方根作為特征參數(shù)訓(xùn)練隱馬爾可夫模型,并將初始?jí)勖鼤r(shí)刻模型作為基準(zhǔn)點(diǎn),計(jì)算性能退化過程模型與基準(zhǔn)點(diǎn)之間的距離,結(jié)果表明隱馬爾可夫模型距離是一種有效的性能退化評(píng)估指標(biāo)。
[Abstract]:Rolling bearing is an important component of rotating machinery. Its performance state plays an important role in the operation and efficiency of mechanical equipment. The intelligent fault diagnosis of rolling bearing based on vibration analysis has become a hot spot of research at home and abroad. However, the related research is mainly aimed at the operating environment of fixed working conditions, and it is difficult to meet the load change in engineering practice. Based on the non-stationary and non Gauss characteristics of the vibration signals of rolling bearings, this paper studies the problems of feature extraction, eigenvector optimization, variable condition fault diagnosis and performance degradation evaluation. The main work is summarized as follows: 1. the continuous wavelet transform of vibration signals is studied, and a kind of continuous wavelet transform is proposed. The Morlet wavelet parameter optimization method based on the minimum Shannon entropy and singular value decomposition. The minimum Shannon entropy means that the wavelet coefficients are sparse, and the high similarity between the wavelets and the signals is guaranteed. The singular value decomposition can detect the periodic characteristics of the sequence. The combination of the two can extract more effective fault feature information. The optimal Morlet wavelet system is studied. The properties of the common statistical parameters, the standard deviation, the mean value, the root mean square and the infinite norm are significant in the different bearing states. As the characteristic parameters, the reliable fault diagnosis results are obtained. Finally, the validity and reliability of the Morlet wavelet parameter optimization are proved by comparative experiments. The difference of the wavelet packet decomposition of the vibration signal is studied by.2.. The eigenvector optimization method combining nature and similarity is used. The wavelet packet decomposition of vibration signals obtains multiple subbands with the same bandwidth, but the effective fault feature information is only distributed in a small number of subbands, and the eigenvectors have redundant information. The Daub8 wavelet is selected to estimate the number of wavelet packet decomposition layers based on the width and harmonic frequency of the subband and the energy of the subband. The feature vector is constructed as a parameter. Based on the Fisher linear distance measure, the row vector of the feature vector with a larger distance in different bearing States is optimized and the row vector with a smaller distance within the feature vector is selected. The comparison experiment shows that the identification accuracy of the optimization method is better than the literature method.3. to study the wavelet denoising of the vibration signal. A robust early fault diagnosis method based on the short-time zero crossing rate is proposed. The zero crossing rate is only related to the frequency of the zero point of the signal, which is independent of the waveform or amplitude, and the working condition is changed. The change of the vibration signal waveform is robust, and can also represent the frequency domain information of the signal to a certain extent. After determining the wavelet function, the decomposition layer number and the threshold strategy, the short time zero crossing rate characteristic of the wavelet denoising signal is studied. The difference is obvious in the different fault state, and it has the larger similarity in the same fault but not at the same time. It is a robust characteristic parameter. Using the data training model of any working condition, the fault types of the current and other three operating conditions can be identified correctly, and the early fault diagnosis.4. is robust to study the non Gauss characteristics of the vibration signal of the rolling bearing and an intelligent fault based on the bispectrum principal component analysis is proposed. The method of diagnosis is to study the bispectrum characteristic of the vibration signal first. Its amplitude and distribution characteristics have distinct difference when the fault types are different, and are similar in the same fault but not at the same time. Using the principal component analysis method to extract the effective feature information in the bispectrum and take its amplitude as the characteristic parameter. Bearing state discrimination of different working conditions and different fault degrees. In addition, the model of data training of zero load condition can identify the other three different types of fault. The fault diagnosis function.5. with robust working condition is used to evaluate the performance degradation of rolling bearings, and a performance degradation evaluation based on Hidden Markov model distance is proposed. First design the rolling bearing acceleration fatigue life test, and record the vibration acceleration signal of the 6205 bearing performance degradation process by the self-made data acquisition system, and study the change law of the common diagnostic index in the process of performance degradation, and find that the performance degradation process of the rolling bearing has gone through six different stages, which is named as the normal state, Early fault, moderate fault, serious fault, early warning stage and bearing failure. The root mean square of the vibration signal is used as a feature parameter to train hidden Markov model, and the initial life time model is used as a reference point to calculate the distance between the performance degradation process model and the base point. The results show that the hidden Markov model distance is an effective way. Performance degradation assessment indicators.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TH133.33

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相關(guān)期刊論文 前2條

1 陶新民;徐晶;劉興麗;劉玉;;基于最大小波奇異譜的軸承故障診斷方法[J];振動(dòng)、測(cè)試與診斷;2010年01期

2 丁建明;林建輝;楊強(qiáng);農(nóng)漢彪;;基于諧波小波奇異熵的軸承故障實(shí)時(shí)診斷[J];中國(guó)機(jī)械工程;2010年01期

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本文編號(hào)：1980341