本文關(guān)鍵詞：強噪聲干擾下行星輪系振動信號分析及其故障診斷技術(shù)研究　出處：《中國礦業(yè)大學(xué)》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 行星輪系 振動信號 預(yù)處理降噪 特征提取 多傳感器 故障診斷

【摘要】：大型復(fù)雜機電裝備涉及煤炭、航天、鋼鐵、船舶、工程機械等重要制造行業(yè),是我國制造業(yè)和工業(yè)發(fā)展的重要基礎(chǔ)。隨著工業(yè)化進程的不斷推進和科學(xué)技術(shù)的快速發(fā)展,大型復(fù)雜機電裝備日益趨向復(fù)雜、可靠、高效、智能化方向發(fā)展。由于行星輪系具有眾多優(yōu)點,現(xiàn)已成為大型復(fù)雜機電裝備傳動系統(tǒng)的重要組成部件。但是行星輪系一般工作于大載荷、強干擾、高污染的惡劣工況,其故障時有發(fā)生,直接影響機電裝備的傳動效率,嚴(yán)重時會導(dǎo)致整個機電裝備失效,甚至人員傷亡等惡劣后果。因此,對行星輪系進行故障診斷研究具有非常重大的意義。但是在真實工況條件下所測得的行星輪系振動信號一般包含強噪聲干擾,并且其特殊的結(jié)構(gòu)及工作方式,也致使行星輪系的故障診斷具有自身的特點和難點。本文以強噪聲干擾下的行星輪系為研究對象,通過對預(yù)處理降噪、故障特征信息提取、特征降維處理、多傳感器融合診斷進行深入研究,形成基于振動信號分析的行星輪系故障診斷技術(shù),為保障行星輪系以及機電裝備傳動系統(tǒng)安全運行提供理論支撐和技術(shù)解決方案。主要內(nèi)容包括:(1)針對行星輪系的具體結(jié)構(gòu)及工作方式,分析了進行行星輪系故障診斷研究的特點和難點;并進行了真實工況條件下強噪聲干擾獲取實驗和不同行星輪系故障狀態(tài)的模擬實驗,獲得了真實工況條件下的強噪聲干擾和不同行星輪系故障狀態(tài)的多傳感器信號。(2)針對行星輪系在真實工況中所遭受的強噪聲干擾,提出了一種結(jié)合雙樹復(fù)小波變換(Dual-Tree Complex Wavelet Transform,DTCWT)和循環(huán)奇異能量差分譜的預(yù)處理降噪方法。利用DTCWT具有較少頻率混疊和頻率泄漏的優(yōu)點,將包含強噪聲干擾的原始振動信號分解到多個具有不同頻率特性的信號中;通過對奇異值分解降噪原理分析,基于級聯(lián)循環(huán)、逐次濾除噪聲的思想提出了循環(huán)奇異能量差分譜降噪方法,根據(jù)不同頻帶噪聲干擾分布特點設(shè)置不同的終止條件實現(xiàn)了對各頻帶信號的降噪處理。利用所提出的預(yù)處理降噪方法,能夠有效消除真實工況強噪聲干擾,保留行星輪系產(chǎn)生的有效信號成分。(3)針對行星輪系所產(chǎn)生的振動信號具有非線性、非平穩(wěn)、強耦合的特性,在充分研究經(jīng)驗?zāi)B(tài)分解(Empirical Mode Decomposition,EMD)和總體經(jīng)驗?zāi)B(tài)分解(Ensemble Empirical Mode Decomposition,EEMD)的基礎(chǔ)上,研究了一種自適應(yīng)噪聲的完備總體經(jīng)驗?zāi)B(tài)分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)算法。并且針對CEEMDAN存在添加高斯白噪聲次數(shù)過多、計算耗時等缺點,提出了一種改進的自適應(yīng)噪聲的完備總體經(jīng)驗?zāi)B(tài)分解(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,ICEEMDAN)算法。一方面在求取本征模態(tài)函數(shù)(Intrinsic Mode Function,IMF)分量過程中,連續(xù)檢測所添加高斯白噪聲對IMF分量的影響,控制在求取IMF分量過程中添加的高斯白噪聲次數(shù),有效減少了非必要高斯白噪聲的添加;另一方面,引入排列熵對IMF分量進行復(fù)雜性檢測,根據(jù)排列熵值變化情況決定是否繼續(xù)需要高斯白噪聲的輔助分解作用。所提出的ICEEMDAN方法能夠有效抑制信號模態(tài)混疊,保證信號分解質(zhì)量和分解的完備性,并且有效減少非必要高斯白噪聲的添加,具有相對更快的計算速度。(4)針對行星輪系產(chǎn)生的微弱故障特征信息,基于行星輪系特征頻率及其相關(guān)成分信噪比建立了有效IMF分量提取準(zhǔn)則,結(jié)合粒子群優(yōu)化(Particle Swarm Optimization,PSO)算法和隨機共振算法構(gòu)建了自適應(yīng)隨機共振系統(tǒng)處理有效IMF分量的重構(gòu)信號,能夠有效提取行星輪系嚙合頻率及其邊頻帶信息,可定性比較判斷行星輪系狀態(tài)。另外,為了量化各IMF分量中的行星輪系特征信息,基于信息熵定義,構(gòu)建了多角度熵特征聯(lián)合提取模型,實現(xiàn)了包含在各IMF分量中故障特征信息的多角度提取量化,形成了全面而綜合的原始故障特征集合。(5)針對原始故障特征集合存在維數(shù)過大、信息冗余和無效特征干擾等情況,開展了基于核方法的高維特征降維研究。在對主元分析(Principal Component Analysis,PCA)、Fisher鑒別分析(Fisher Discriminant Analysis,FDA)和核主元分析(Kernel Principal Component Analysis,KPCA)研究基礎(chǔ)上,研究了一種優(yōu)化參數(shù)的核Fisher鑒別分析(Kernel Fisher Discriminant Analysis,KFDA)方法。根據(jù)核特征空間的類間散度矩陣和類內(nèi)散度矩陣關(guān)系,定義了類對可分性和類別可分性,進而形成了基于類別可分性的KFDA核參數(shù)優(yōu)化選取準(zhǔn)則,解決了KFDA的核參數(shù)選擇問題。并且利用優(yōu)化參數(shù)的KFDA方法能夠有效完成對原始故障特征集合的特征融合和降維處理,提取出敏感故障特征。(6)針對真實工況強噪聲干擾下,基于單傳感器進行故障診斷容易引起信息缺失、出現(xiàn)識別不確定性、故障診斷精度降低和診斷信任程度降低等情況,開展了多傳感器融合診斷研究�；谒崛〉拿舾泄收咸卣骱蜆O限學(xué)習(xí)機(Extreme Learning Machine,ELM)獲得了單傳感器產(chǎn)生的局部診斷結(jié)論,提出了基于ELM誤差距離的基本信任函數(shù)分配方法,并建立了基于證據(jù)沖突檢測的D-S證據(jù)理論融合規(guī)則。通過本文所建立的方法對強噪聲干擾下的行星輪系進行多傳感器融合診斷,可消除識別的不確定性,處理證據(jù)沖突情況,有效提高故障診斷精度和診斷信任程度。文章最后對論文的工作進行了總結(jié),并對相關(guān)的研究技術(shù)進行了展望。
[Abstract]:Large-scale complex electromechanical equipment involving coal, steel, shipbuilding, aerospace, engineering machinery and other important manufacturing industry, is an important basic industry and manufacturing industry development in our country. With the rapid development of the industrialization process of science and technology, large-scale complex electromechanical equipment to more complex, reliable, efficient, intelligent direction because. The planetary gear has many advantages, has become an important component of large-scale complex electromechanical equipment transmission system. But planetary gear trains generally work in large load, strong interference, harsh working conditions and high pollution, the fault occurred, directly affect the transmission efficiency of electrical equipment, which can lead to the mechanical and electrical equipment failure, even casualties bad consequences. Therefore, it has very important meaning to research on fault diagnosis of planetary gear planetary gear vibration test. The trust in the real conditions but General contains strong noise interference, and its special structure and working mode, has its own characteristics and difficulties of the fault diagnosis of planetary gear train. This paper also leads to noise of the planetary gear train as the research object, through the pretreatment of noise reduction, fault feature extraction, feature dimension reduction, multi-sensor fusion diagnosis study the formation of planetary gear fault diagnosis based on vibration signal analysis, provide theoretical support and technical solutions for the protection of the planetary gear transmission system and mechanical and electrical equipment safety operation. The main content includes: (1) the specific structure and operation mode of the planetary gear train, analyzes the characteristics and difficulties of gear fault diagnosis on the planet true; and make a simulation experiment under the condition of strong noise and obtain the different planetary gear fault condition, get real conditions Multi sensor signal in strong noise and different planetary gear fault state. (2) according to the strong noise of planetary gear train suffered in the real conditions, proposed a combination of dual tree complex wavelet transform (Dual-Tree Complex Wavelet Transform, DTCWT) and the odd cycle ability differential spectral preprocessing denoising method. Using the DTCWT has less of the mixed frequency aliasing and frequency leakage advantages, will contain the original vibration signal in strong noise interference is decomposed into multiple signals with different frequency characteristics; based on singular value decomposition and de-noising principle analysis, based on the cascade cycle noise, successive proposed cyclic singular energy difference spectrum denoising method based on noise reduction. Different band noise distribution characteristics of different set termination conditions realized on each band signal. By pretreatment denoising method proposed can effectively eliminate it. The real condition of strong noise, retain the effective signal component of planetary gear train generated. (3) with nonlinear vibration signal generated according to the non-stationary characteristics of planetary gear train, strong coupling, on the basis of the empirical mode decomposition (Empirical Mode, Decomposition, EMD) and ensemble empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD). Based on the complete modal analysis of the overall experience of an adaptive noise decomposition (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, CEEMDAN) and CEEMDAN algorithm. For there are too many added Gauss white noise frequency, computation time and other shortcomings, put forward a complete overall empirical mode of an improved adaptive noise decomposition (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise ICEEMDAN) algorithm. On the one hand, to obtain the intrinsic mode function (Intrinsi C Mode Function, IMF) component process, continuous detection of the added influence of Gauss white noise on the IMF component, control the Gauss white noise added in the times of seeking IMF component process, effectively reduce the need to add non Gauss white noise; on the other hand, the introduction of permutation entropy complexity detection of IMF component, according to the permutation entropy change the decision whether to continue to assist the Gauss white noise decomposition. ICEEMDAN the proposed method can effectively suppress the signal modal aliasing signal decomposition and decomposition of the quality assurance system, and effectively reduce the need to add non Gauss white noise, with relatively faster computing speed. (4) the weak fault information according to the characteristics of planetary gear, planetary gear train frequency and related components of SNR based on the establishment of an effective IMF component extraction criteria based on particle swarm optimization (Particle Swarm Optimiza Tion, PSO) algorithm and stochastic resonance algorithm is constructed to reconstruct the signal adaptive stochastic resonance system effectively with the IMF component, which can effectively extract the meshing planetary gear train frequency and side band information, can be compared to determine the state of planetary gear train. In addition, in order to quantify the characteristics of planetary gear train information component of each IMF, based on the definition of information entropy, construct multi angle joint entropy feature extraction model, realizes the multi angle containing fault feature information in the IMF component extraction in quantification, formed a comprehensive and comprehensive collection of original fault feature. (5) according to the original fault feature set in high dimension, information redundancy and invalid feature interference etc., carried out to reduce the dimension of the high feature based on kernel method. In the analysis of PCA (Principal Component Analysis, PCA), Fisher (Fisher Discriminant Analysis discriminant analysis, FDA) and Kernel (kernel principal component analysis Principal Component Analysis, KPCA) on the basis of an optimized kernel Fisher discriminant analysis of parameters (Kernel Fisher Discriminant Analysis, KFDA) method. According to the kernel feature space between class scatter matrix and within class scatter matrix, we define a class of separable and separability, and the formation of the parameter of KFDA kernel the optimization selection criterion of separability based on solving the KFDA kernel parameter selection problem. And the use of KFDA optimization method can effectively complete the fusion feature set on the original fault feature and reduce dimension, extract the sensitive fault features. (6) according to the actual condition of strong noise, easy to cause the single sensor fault diagnosis the lack of information based on the recognition of uncertainty, fault diagnosis and diagnosis accuracy reduce the degree of trust decreased, launched a multi sensor fusion study based on diagnosis. The extraction of fault feature sensitive and extreme learning machine (Extreme Learning Machine, ELM) and obtain the local diagnosis of single sensor production, puts forward the basic belief function assignment method for ELM errors based on distance, and the establishment of evidence conflict detection D-S evidence theory fusion rules based on the method proposed in this paper. Based on strong noise the planetary gear train for multi sensor fusion diagnosis, which can eliminate the uncertainty of identification, handling of evidence conflict, effectively improve the accuracy of fault diagnosis and diagnosis of the degree of trust. At the end of the thesis sums up, and the related research technology is prospected.

【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TH132.425
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本文編號：1404839