基于稀疏分解及圖像稀疏表征的滾動(dòng)軸承微弱故障診斷
發(fā)布時(shí)間：2018-11-22 07:44
【摘要】：作為國民經(jīng)濟(jì)支柱型企業(yè)中的關(guān)鍵大型現(xiàn)代化旋轉(zhuǎn)機(jī)械設(shè)備,對(duì)其工作及運(yùn)行環(huán)境苛刻性的要求越來越高,同時(shí)對(duì)保證其長期安全運(yùn)行的監(jiān)測機(jī)制要求也愈來愈高。設(shè)備能否安全運(yùn)行不僅牽涉到企業(yè)經(jīng)濟(jì)利益,而且關(guān)系到操作設(shè)備員工的生命財(cái)產(chǎn)的安全保證與否。能否有效提取出旋轉(zhuǎn)機(jī)械的微弱故障特征,進(jìn)而制定有效的針對(duì)治理措施以確保設(shè)備的安全高效運(yùn)行顯得尤為重要。作為旋轉(zhuǎn)機(jī)械中廣泛應(yīng)用的零部件-滾動(dòng)軸承,其安全運(yùn)行與否往往決定著整個(gè)設(shè)備能否安全運(yùn)行。對(duì)滾動(dòng)軸承進(jìn)行有效、及時(shí)的故障診斷有著非常重要的安全及經(jīng)濟(jì)意義。然而,實(shí)際工程應(yīng)用中滾動(dòng)軸承的故障特征往往表現(xiàn)得非常微弱,究其原因無異于以下三種情況:采集路徑較長以致信號(hào)衰減嚴(yán)重;早期微弱故障階段及其他噪源干擾嚴(yán)重;復(fù)合故障狀態(tài)下。研究上述三種情況下滾動(dòng)軸承的故障診斷方法有著重要的實(shí)際工程應(yīng)用價(jià)值及安全經(jīng)濟(jì)意義。滾動(dòng)軸承發(fā)生故障時(shí)其振動(dòng)信號(hào)往往呈現(xiàn)出非高斯、非平穩(wěn)及非線性特性,傳統(tǒng)的信號(hào)處理方法不能再有效提取出滾動(dòng)軸承發(fā)生故障時(shí)的非線性、非高斯特征。稀疏分解方法是一種能有效匹配滾動(dòng)軸承發(fā)生故障時(shí)沖擊信號(hào)特征的處理方法,并在滾動(dòng)軸承的故障診斷中已經(jīng)取得了一定應(yīng)用�；诖�,本文在經(jīng)典稀疏分解方法的基礎(chǔ)上提出改進(jìn)方法對(duì)滾動(dòng)軸承微弱故障診斷進(jìn)行深入的理論及實(shí)驗(yàn)研究;借鑒圖像非負(fù)矩陣分解處理的思想,將非負(fù)矩陣分解方法與稀疏分解的思想相結(jié)合,提出基于雙譜圖像稀疏性非負(fù)矩陣分解的滾動(dòng)軸承復(fù)合故障診斷方法。論文的主要內(nèi)容包括以下幾個(gè)方面:(1)首先以旋轉(zhuǎn)機(jī)械微弱故障特征提取所面臨的理論及實(shí)際問題為出發(fā)點(diǎn),闡述本學(xué)位論文的研究背景及其相關(guān)重要意義�？偨Y(jié)近年來關(guān)于機(jī)械設(shè)備的相關(guān)故障診斷方法、智能診斷方法以及圖像稀疏表征等方面的國內(nèi)外研究現(xiàn)狀并分析所總結(jié)方法的利弊,確立論文研究內(nèi)容。(2)詳細(xì)介紹了稀疏分解的基本思想、基礎(chǔ)數(shù)學(xué)理論、常用的典型求解算法、稀疏性度量及冗余字典的構(gòu)建等內(nèi)容;簡要介紹基于稀疏分解思想的圖像稀疏表征的發(fā)展歷程,并對(duì)圖像稀疏表征的多尺度幾何分析方法作以詳細(xì)的介紹。此章節(jié)的內(nèi)容為后續(xù)章節(jié)具體研究內(nèi)容奠定堅(jiān)實(shí)的理論支撐。(3)實(shí)際工程應(yīng)用中,某些設(shè)備在安裝振動(dòng)傳感器時(shí)由于受實(shí)際條件的限制,造成信號(hào)采集路徑較長(傳感器安裝位置所采集到的振動(dòng)信號(hào)離實(shí)際故障振源比較遠(yuǎn))以致信號(hào)衰減嚴(yán)重及受背景噪聲影響比較大,直接對(duì)此工況下采集到的信號(hào)進(jìn)行故障特征提取很難取得好的效果。最小熵解卷積(Minimum Entropy De-convolution,MED)方法有效減弱了采集路徑信號(hào)衰減的影響,能有效突出滾動(dòng)軸承發(fā)生故障時(shí)的瞬態(tài)沖擊成份;稀疏分解算法能用最佳的原子去有效的匹配滾動(dòng)軸承發(fā)生故障時(shí)的瞬態(tài)沖擊成份。將二者的優(yōu)點(diǎn)相結(jié)合用于滾動(dòng)軸承的微弱故障特征提取,提出基于MED-稀疏分解的滾動(dòng)軸承微弱故障診斷方法,通過仿真和實(shí)驗(yàn)驗(yàn)證了所述方法的有效性及優(yōu)點(diǎn)。并比較了所述方法相對(duì)于小波分析方法、總體經(jīng)驗(yàn)?zāi)B(tài)分解方法、時(shí)頻切片小波變換方法及基于譜峭度處理方法的優(yōu)點(diǎn)。(4)共振稀疏分解方法是一種基于多字典庫的稀疏分解方法,可以同時(shí)分解出信號(hào)中的瞬態(tài)沖擊成分及其持續(xù)震蕩成分(工頻及其諧頻成分)。該方法在EEMD前處理基礎(chǔ)上,對(duì)分解后峭度指標(biāo)最大的固有模態(tài)函數(shù)分量進(jìn)行共振稀疏分解分析:根據(jù)共振稀疏分解中信號(hào)品質(zhì)因子的定義,分別構(gòu)建高、低品質(zhì)因子小波基函數(shù)字典庫、并利用形態(tài)學(xué)分析方法建立信號(hào)稀疏表示的目標(biāo)函數(shù)進(jìn)而實(shí)現(xiàn)對(duì)滾動(dòng)軸承發(fā)生早期微弱故障或受其他高品質(zhì)因子噪源干擾嚴(yán)重時(shí)具有低品質(zhì)因子的瞬態(tài)故障成份及其他持續(xù)振蕩高品質(zhì)因子噪聲成份的成功分離。(5)滾動(dòng)軸承發(fā)生復(fù)合故障時(shí),由于不同部位故障信號(hào)之間的相互干擾及耦合效應(yīng),復(fù)合故障信號(hào)表現(xiàn)得非常復(fù)雜,基于信號(hào)處理的滾動(dòng)軸承復(fù)合故障方法往往難以取得好的效果。雙譜三維圖像信息比單純頻譜蘊(yùn)含更多故障信息,適用于滾動(dòng)軸承復(fù)合故障特征提取。但是如何有效精煉的提取三維圖譜的特征以實(shí)現(xiàn)智能診斷是一個(gè)亟需解決的問題�；诖�,將圖像非負(fù)矩陣分解與稀疏分解的思想相結(jié)合,提出稀疏性非負(fù)矩陣分解方法對(duì)雙譜三維圖像進(jìn)行有效特征提取進(jìn)而實(shí)現(xiàn)滾動(dòng)軸承復(fù)合故障的高效智能診斷。最后并與基于雙譜圖像非負(fù)矩陣分解的特征提取效果作以對(duì)比突出了所述方法的優(yōu)越性。
[Abstract]:As the key large-scale modern rotating machinery in the national economy pillar-type enterprise, the requirement for its working and operating environment is higher and higher, and the monitoring mechanism for ensuring its long-term safe operation is getting higher and higher. Whether the equipment can operate safely involves not only the economic benefits of the enterprise, but also the safety guarantee of the life and property of the employees of the operation equipment. It is very important to effectively extract the weak fault features of the rotating machinery and to develop effective measures to ensure the safe and efficient operation of the equipment. As a part-rolling bearing widely used in the rotating machinery, the safe operation of the rolling bearing often determines whether the whole equipment can operate safely. It has very important safety and economic significance for the effective and timely fault diagnosis of rolling bearing. However, the fault characteristics of rolling bearing in practical engineering application are often very weak, the reason is as follows: the acquisition path is longer so that the signal attenuation is serious; the early weak failure stage and other noise source interference are serious; and in the composite fault condition. The method of fault diagnosis of rolling bearing is of great value and safety and economic significance in three cases. The non-Gaussian, non-stationary and non-linear characteristics of the rolling bearing are often presented in the fault of the rolling bearing, and the traditional signal processing method can not extract the non-linear and non-Gaussian features of the rolling bearing failure. The sparse decomposition method is a kind of treatment method which can effectively match the characteristics of the impact signal when the rolling bearing is in fault, and has obtained some application in the fault diagnosis of the rolling bearing. On the base of the classical sparse decomposition method, this paper makes an in-depth theoretical and experimental study on the weak fault diagnosis of the rolling bearing, and combines the non-negative matrix decomposition method with the idea of sparse decomposition based on the thought of the non-negative matrix decomposition processing of the image. The invention provides a rolling bearing composite fault diagnosis method based on a double-spectrum image sparse non-negative matrix decomposition. The main contents of the thesis include the following aspects: (1) Firstly, the thesis starts with the theory and practical problems of the weak fault feature extraction of the rotating machinery, and expounds the research background and relevant significance of the thesis. The present situation of relevant fault diagnosis method, intelligent diagnosis method and image sparse representation of mechanical equipment in recent years are summarized, and the advantages and disadvantages of the summarized methods are analyzed and the research contents of the paper are established. (2) The basic idea of the sparse decomposition, the basic mathematical theory, the typical solution algorithm, the sparsity measure and the construction of the redundant dictionary are introduced in detail, and the development course of the sparse representation of the image based on the sparse decomposition is briefly introduced. and the multi-scale geometric analysis method for sparse representation of the images is described in detail. The contents of this section provide a solid theoretical support for the specific study of the subsequent sections. (3) In practical engineering applications, certain equipment is limited by the actual conditions when the vibration sensor is installed, The signal acquisition path is long (the vibration signal acquired by the sensor installation position is far from the actual fault source) so that the signal attenuation is serious and the influence of the background noise is relatively large, and the signal that is collected directly under the condition is difficult to obtain good effect. The Minimum Entropy De-convolute (MED) method effectively reduces the influence of the signal attenuation of the acquisition path, and can effectively highlight the transient impact component in the fault of the rolling bearing; the sparse decomposition algorithm can effectively match the transient impact component in the fault of the rolling bearing with the best atom. The advantages of the method are verified by simulation and experiment by combining the advantages of the two with the weak fault feature extraction of the rolling bearing. The method based on the wavelet analysis method, the general empirical mode decomposition method, the time-frequency slice small-wave transformation method and the spectral kurtosis processing method are compared and compared. and (4) the resonance sparse decomposition method is a sparse decomposition method based on a multi-dictionary library, and can simultaneously decompose the transient impact component in the signal and the continuous oscillation component (power frequency and the harmonic frequency component thereof). according to the definition of the signal quality factor in the resonance sparse decomposition, a high and low quality factor wavelet basis function dictionary library is respectively constructed, and a morphological analysis method is used to establish a target function of the signal sparse representation, so as to realize the success of the transient fault component with low quality factor and other continuous oscillation high-quality factor noise components in the early-stage weak fault of the rolling bearing or the interference of other high-quality factor noise sources. It's out of here. (5) The composite fault signal is very complex due to the mutual interference and coupling effect between the fault signals of different parts, and the composite fault method of the rolling bearing based on the signal processing is often difficult to achieve. The double-spectrum three-dimensional image information contains more fault information than the pure frequency spectrum, and is suitable for the feature extraction of the composite fault of the rolling bearing. But how to extract the characteristics of the three-dimensional map effectively to realize the intelligent diagnosis is an urgent problem to be solved. On the basis of this, combining the non-negative matrix decomposition of the image with the idea of sparse decomposition, a sparse non-negative matrix decomposition method is proposed to carry out effective feature extraction on the double-spectrum three-dimensional image, so as to realize the high-efficiency intelligent diagnosis of the composite fault of the rolling bearing. Finally, the advantages of the method are compared with the feature extraction effect based on the non-negative matrix decomposition of the dual-spectrum image.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TH133.33
本文編號(hào)：2348550
[Abstract]:As the key large-scale modern rotating machinery in the national economy pillar-type enterprise, the requirement for its working and operating environment is higher and higher, and the monitoring mechanism for ensuring its long-term safe operation is getting higher and higher. Whether the equipment can operate safely involves not only the economic benefits of the enterprise, but also the safety guarantee of the life and property of the employees of the operation equipment. It is very important to effectively extract the weak fault features of the rotating machinery and to develop effective measures to ensure the safe and efficient operation of the equipment. As a part-rolling bearing widely used in the rotating machinery, the safe operation of the rolling bearing often determines whether the whole equipment can operate safely. It has very important safety and economic significance for the effective and timely fault diagnosis of rolling bearing. However, the fault characteristics of rolling bearing in practical engineering application are often very weak, the reason is as follows: the acquisition path is longer so that the signal attenuation is serious; the early weak failure stage and other noise source interference are serious; and in the composite fault condition. The method of fault diagnosis of rolling bearing is of great value and safety and economic significance in three cases. The non-Gaussian, non-stationary and non-linear characteristics of the rolling bearing are often presented in the fault of the rolling bearing, and the traditional signal processing method can not extract the non-linear and non-Gaussian features of the rolling bearing failure. The sparse decomposition method is a kind of treatment method which can effectively match the characteristics of the impact signal when the rolling bearing is in fault, and has obtained some application in the fault diagnosis of the rolling bearing. On the base of the classical sparse decomposition method, this paper makes an in-depth theoretical and experimental study on the weak fault diagnosis of the rolling bearing, and combines the non-negative matrix decomposition method with the idea of sparse decomposition based on the thought of the non-negative matrix decomposition processing of the image. The invention provides a rolling bearing composite fault diagnosis method based on a double-spectrum image sparse non-negative matrix decomposition. The main contents of the thesis include the following aspects: (1) Firstly, the thesis starts with the theory and practical problems of the weak fault feature extraction of the rotating machinery, and expounds the research background and relevant significance of the thesis. The present situation of relevant fault diagnosis method, intelligent diagnosis method and image sparse representation of mechanical equipment in recent years are summarized, and the advantages and disadvantages of the summarized methods are analyzed and the research contents of the paper are established. (2) The basic idea of the sparse decomposition, the basic mathematical theory, the typical solution algorithm, the sparsity measure and the construction of the redundant dictionary are introduced in detail, and the development course of the sparse representation of the image based on the sparse decomposition is briefly introduced. and the multi-scale geometric analysis method for sparse representation of the images is described in detail. The contents of this section provide a solid theoretical support for the specific study of the subsequent sections. (3) In practical engineering applications, certain equipment is limited by the actual conditions when the vibration sensor is installed, The signal acquisition path is long (the vibration signal acquired by the sensor installation position is far from the actual fault source) so that the signal attenuation is serious and the influence of the background noise is relatively large, and the signal that is collected directly under the condition is difficult to obtain good effect. The Minimum Entropy De-convolute (MED) method effectively reduces the influence of the signal attenuation of the acquisition path, and can effectively highlight the transient impact component in the fault of the rolling bearing; the sparse decomposition algorithm can effectively match the transient impact component in the fault of the rolling bearing with the best atom. The advantages of the method are verified by simulation and experiment by combining the advantages of the two with the weak fault feature extraction of the rolling bearing. The method based on the wavelet analysis method, the general empirical mode decomposition method, the time-frequency slice small-wave transformation method and the spectral kurtosis processing method are compared and compared. and (4) the resonance sparse decomposition method is a sparse decomposition method based on a multi-dictionary library, and can simultaneously decompose the transient impact component in the signal and the continuous oscillation component (power frequency and the harmonic frequency component thereof). according to the definition of the signal quality factor in the resonance sparse decomposition, a high and low quality factor wavelet basis function dictionary library is respectively constructed, and a morphological analysis method is used to establish a target function of the signal sparse representation, so as to realize the success of the transient fault component with low quality factor and other continuous oscillation high-quality factor noise components in the early-stage weak fault of the rolling bearing or the interference of other high-quality factor noise sources. It's out of here. (5) The composite fault signal is very complex due to the mutual interference and coupling effect between the fault signals of different parts, and the composite fault method of the rolling bearing based on the signal processing is often difficult to achieve. The double-spectrum three-dimensional image information contains more fault information than the pure frequency spectrum, and is suitable for the feature extraction of the composite fault of the rolling bearing. But how to extract the characteristics of the three-dimensional map effectively to realize the intelligent diagnosis is an urgent problem to be solved. On the basis of this, combining the non-negative matrix decomposition of the image with the idea of sparse decomposition, a sparse non-negative matrix decomposition method is proposed to carry out effective feature extraction on the double-spectrum three-dimensional image, so as to realize the high-efficiency intelligent diagnosis of the composite fault of the rolling bearing. Finally, the advantages of the method are compared with the feature extraction effect based on the non-negative matrix decomposition of the dual-spectrum image.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TH133.33
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 ;Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain[J];Science China(Information Sciences);2012年08期
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