基于支持向量回歸的軸承故障定量診斷方法研究
發(fā)布時間：2018-10-24 14:35
【摘要】：機械設(shè)備運行條件復(fù)雜、環(huán)境惡劣的工況下，其核心零部件和重要機械結(jié)構(gòu)會不可避免地發(fā)生不同程度的故障。機械設(shè)備一旦出現(xiàn)故障，可能會帶來巨大的經(jīng)濟(jì)損失和人員傷亡。然而機械設(shè)備故障的演變存在一個由輕微到嚴(yán)重的發(fā)展過程。因而準(zhǔn)確及時識別運行過程中萌生和演變過程，對保障機械設(shè)備安全運行、避免經(jīng)濟(jì)損失和災(zāi)難性事故意義重大。故障定量診斷方法是一種能夠有效的確定故障演變歷程和故障大小的機械故障診斷方法。本文以軸承為對象，針對故障狀態(tài)特征與故障大小之間的高度非線性、故障樣本少等特點，提出采用支持向量回歸機方法，建立軸承故障嚴(yán)重程度判斷模型和故障大小定量診斷研究方案。 首先，，進(jìn)行了軸承常見的故障失效形式分析以及滾動軸承故障的運動學(xué)研究，并介紹了本文的軸承故障模擬實驗系統(tǒng)，在此系統(tǒng)上進(jìn)行了不同程度的軸承外圈故障試驗。 然后，系統(tǒng)的闡述了統(tǒng)計學(xué)習(xí)理論，介紹了基于結(jié)構(gòu)風(fēng)險最小化原則的支持向量機理論，并進(jìn)一步提出將支持向量分類機和支持向量回歸機用于故障特征分類和回歸描述，為故障診斷模型的建立提供了理論基礎(chǔ)。研究了軸承振動信號故障特征的提取方法，提取了相關(guān)的特征如峰值、方差、峭度等，并且分析了這些特征與故障大小之間的關(guān)系，為故障診斷模型的建立提供了數(shù)據(jù)基礎(chǔ)。 最后根據(jù)所提取的特征運用支持向量回歸機分別建立了軸承故障程度的分類模型和故障大小的定量診斷模型，分類模型用于定量評價故障程度，定量診斷模型用于確定故障大小。方法在訓(xùn)練集和測試集上的效果驗證了該方法的有效性。 本文基于軸承振動信號特征，采用支持向量回歸機建立了軸承故障診斷模型，包括了故障程度分類模型和故障大小定量診斷模型，為軸承故障程度分類和定量診斷確立了有效的方法。 本論文依托于江蘇省自然科學(xué)基金項目(批準(zhǔn)號: BK2010225)：“基于瞬態(tài)振動特征辨識的軸承局部故障定量診斷研究”。
[Abstract]:When the operating conditions of machinery and equipment are complex and the environment is bad, the failure of its core parts and important mechanical structures will inevitably occur in varying degrees. Once the mechanical equipment breaks down, it may bring huge economic losses and casualties. However, the evolution of mechanical failure has a slight to serious development process. Therefore, it is of great significance to accurately and timely identify the process of initiation and evolution in the operation process to ensure the safe operation of mechanical equipment and to avoid economic losses and catastrophic accidents. The quantitative fault diagnosis method is a kind of mechanical fault diagnosis method which can effectively determine the fault evolution history and fault size. This paper takes the bearing as the object, aiming at the high nonlinearity between the fault state characteristic and the fault size, and so on, and puts forward the support vector regression machine method. The model of bearing fault severity and the research scheme of fault size quantitative diagnosis are established. Firstly, the common failure forms of bearing and the kinematics of rolling bearing fault are analyzed, and the bearing fault simulation experiment system is introduced in this paper, and the bearing outer ring fault test is carried out on the system. Then, the statistical learning theory is systematically expounded, and the support vector machine theory based on structural risk minimization principle is introduced. Furthermore, support vector classification machine and support vector regression machine are proposed for fault feature classification and regression description. It provides a theoretical basis for the establishment of fault diagnosis model. The fault feature extraction method of bearing vibration signal is studied, and the correlation features such as peak value, variance and kurtosis are extracted, and the relationship between these features and fault size is analyzed, which provides a data basis for the establishment of fault diagnosis model. Finally, according to the extracted features, the classification model of bearing fault degree and the quantitative diagnosis model of fault size are established by using support vector regression machine, and the classification model is used to quantitatively evaluate the fault degree. The quantitative diagnosis model is used to determine the fault size. The effectiveness of the method is verified on the training set and test set. Based on the characteristics of bearing vibration signal, the bearing fault diagnosis model is established by using support vector regression machine, which includes the classification model of fault degree and the quantitative diagnosis model of fault size. An effective method for classification and quantitative diagnosis of bearing fault degree is established. This paper is based on the project of Jiangsu Provincial Natural Science Foundation (Grant No.: BK2010225): "quantitative diagnosis of bearing local faults based on transient vibration feature identification".
【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3
[Abstract]:When the operating conditions of machinery and equipment are complex and the environment is bad, the failure of its core parts and important mechanical structures will inevitably occur in varying degrees. Once the mechanical equipment breaks down, it may bring huge economic losses and casualties. However, the evolution of mechanical failure has a slight to serious development process. Therefore, it is of great significance to accurately and timely identify the process of initiation and evolution in the operation process to ensure the safe operation of mechanical equipment and to avoid economic losses and catastrophic accidents. The quantitative fault diagnosis method is a kind of mechanical fault diagnosis method which can effectively determine the fault evolution history and fault size. This paper takes the bearing as the object, aiming at the high nonlinearity between the fault state characteristic and the fault size, and so on, and puts forward the support vector regression machine method. The model of bearing fault severity and the research scheme of fault size quantitative diagnosis are established. Firstly, the common failure forms of bearing and the kinematics of rolling bearing fault are analyzed, and the bearing fault simulation experiment system is introduced in this paper, and the bearing outer ring fault test is carried out on the system. Then, the statistical learning theory is systematically expounded, and the support vector machine theory based on structural risk minimization principle is introduced. Furthermore, support vector classification machine and support vector regression machine are proposed for fault feature classification and regression description. It provides a theoretical basis for the establishment of fault diagnosis model. The fault feature extraction method of bearing vibration signal is studied, and the correlation features such as peak value, variance and kurtosis are extracted, and the relationship between these features and fault size is analyzed, which provides a data basis for the establishment of fault diagnosis model. Finally, according to the extracted features, the classification model of bearing fault degree and the quantitative diagnosis model of fault size are established by using support vector regression machine, and the classification model is used to quantitatively evaluate the fault degree. The quantitative diagnosis model is used to determine the fault size. The effectiveness of the method is verified on the training set and test set. Based on the characteristics of bearing vibration signal, the bearing fault diagnosis model is established by using support vector regression machine, which includes the classification model of fault degree and the quantitative diagnosis model of fault size. An effective method for classification and quantitative diagnosis of bearing fault degree is established. This paper is based on the project of Jiangsu Provincial Natural Science Foundation (Grant No.: BK2010225): "quantitative diagnosis of bearing local faults based on transient vibration feature identification".
【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3
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