本文選題：獨立分量分析 + 稀疏分量分析��； 參考：《昆明理工大學(xué)》2011年碩士論文

【摘要】：在旋轉(zhuǎn)機械振動狀態(tài)監(jiān)測與故障診斷過程中,常常面臨著各種干擾或者各種故障相互耦合的情況,研究如何從復(fù)雜的監(jiān)測信號當中分離提取故障特征具有重要意義。本文以旋轉(zhuǎn)機械振動信號為研究對象,以盲源分離(BSS)為研究方法,系統(tǒng)地研究了三種盲源分離技術(shù)及其在機械故障診斷領(lǐng)域中的應(yīng)用,重點針對滾動軸承復(fù)合故障分離問題,結(jié)合形態(tài)濾波技術(shù)研究了獨立分量分析(ICA)及稀疏分量分析(SCA)在實際滾動軸承復(fù)合故障當中的分離方法。 本文主要研究內(nèi)容如下： (1)以機械設(shè)備故障診斷行業(yè)為背景,對盲源分離方法中的三種典型分析方法-獨立分量分析、稀疏分量分析以及非負矩陣分解(NMF)的相關(guān)研究現(xiàn)狀進行了簡要綜述,并總結(jié)了盲分離方法在機械故障診斷領(lǐng)域的研究應(yīng)用情況。 (2)在研究獨立分量分析相關(guān)理論基礎(chǔ)之上,針對常用梯度算法易使優(yōu)化問題陷入局部最優(yōu),且步長選擇對算法速度影響較大的問題,將基于微粒群優(yōu)化的獨立分量分析應(yīng)用到旋轉(zhuǎn)機械轉(zhuǎn)子復(fù)合故障診斷當中。 (3)針對滾動軸承復(fù)合故障各個故障相互耦合,振動信號成分復(fù)雜,常用盲源分離方法不能有效分離各故障特征的問題,以實驗分析的方法探討了滾動軸承復(fù)合故障的耦合機制,并結(jié)合滾動軸承故障振動信號所呈現(xiàn)的非平穩(wěn)、高頻調(diào)制等特性,提出一種基于形態(tài)濾波的獨立分量分析方法,并將其應(yīng)用于滾動軸承復(fù)合故障信號分離中。 (4)針對多數(shù)盲信號處理方法在分離信號的過程中,要求觀測信號數(shù)目大于等于源信號數(shù)目的問題,將欠定盲分離處理方法中應(yīng)用較為成功的稀疏分量分析(SCA)引用到滾動軸承復(fù)合故障診斷當中。針對軸承故障信號難以滿足稀疏分量分析所要求的信號需要呈稀疏分布的問題,提出一種基于形態(tài)濾波的稀疏分量分析方法,并分別在完備與欠定的條件下對滾動軸承復(fù)合故障進行了實驗研究。 (5)研究了非負矩陣分解及其在盲分離中的應(yīng)用,并嘗試將其應(yīng)用于滾動軸承故障診斷當中。 (6)在理論與實驗研究的基礎(chǔ)上,利用MATLAB設(shè)計實現(xiàn)了三種盲分離技術(shù)在旋轉(zhuǎn)機械復(fù)合故障診斷中的應(yīng)用系統(tǒng)。并分別采用仿真與實驗的方法對系統(tǒng)進行了驗證。
[Abstract]:In the process of vibration state monitoring and fault diagnosis of rotating machinery, it is often faced with various disturbances or mutual coupling of various faults. It is of great significance to study how to separate and extract fault features from complex monitoring signals.In this paper, three blind source separation techniques and their applications in the field of mechanical fault diagnosis are systematically studied, with the vibration signal of rotating machinery as the research object and BSS as the research method. The emphasis is on the composite fault separation of rolling bearings.The separation methods of independent component analysis (ICA) and sparse component analysis (SCA) in the composite fault of rolling bearing are studied by means of morphological filtering technique.The main contents of this paper are as follows:1) based on the background of mechanical equipment fault diagnosis industry, this paper briefly reviews the research status of three typical analysis methods in blind source separation: independent component analysis (ICA), sparse component analysis (SAA) and nonnegative matrix factorization (NMFs).The research and application of blind separation in the field of mechanical fault diagnosis are summarized.2) based on the research of the theory of independent component analysis (ICA), aiming at the problem that the common gradient algorithm is easy to make the optimization problem fall into the local optimum, and the selection of step size has a great influence on the speed of the algorithm,The independent component analysis (ICA) based on particle swarm optimization (PSO) is applied to rotor complex fault diagnosis of rotating machinery.In view of the problems that each fault of rolling bearing composite fault is coupled with each other, the vibration signal component is complex, and the common blind source separation method can not effectively separate the fault characteristics, the coupling mechanism of the rolling bearing composite fault is discussed by the method of experimental analysis.Based on the characteristics of non-stationary and high-frequency modulation of rolling bearing fault vibration signal, an independent component analysis method based on morphological filtering is proposed and applied to the separation of rolling bearing composite fault signal.Aiming at the problem that most blind signal processing methods require the number of observed signals to be greater than or equal to the number of source signals in the process of separating signals,The sparse component analysis (SCA), which has been successfully applied in the under-determined blind separation method, is applied to the composite fault diagnosis of rolling bearings.Aiming at the problem that the bearing fault signal is difficult to meet the need of sparse component analysis (SAA), a sparse component analysis method based on morphological filtering is proposed.The complex fault of rolling bearing is studied experimentally under the condition of complete and underdetermined.In this paper, the nonnegative matrix decomposition and its application in blind separation are studied and applied to the fault diagnosis of rolling bearings.On the basis of theoretical and experimental research, three blind separation techniques are designed and implemented by using MATLAB in complex fault diagnosis system of rotating machinery.Simulation and experiment are used to verify the system.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3;TN911.7

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