【摘要】：本論文研究的目的旨在尋找一種適合于空氣壓縮機的、高效可行的故障診斷方法。本文從空氣壓縮機的工作原理、機械構(gòu)造、故障類型以及故障機理出發(fā),總結(jié)了空氣壓縮機的主要故障特點,并歸納了空氣壓縮機故障診斷系統(tǒng)應(yīng)滿足的實現(xiàn)要求。 在尋找合適的故障診斷算法時,本文將重點放在如何解決空氣壓縮機故障檢測變量過多和檢測變量之間存在高相關(guān)性的問題上。為解決這一問題,本文采用了主元分析技術(shù)(Principal Components Analysis, PCA)作為檢測數(shù)據(jù)的預(yù)處理算法。該算法通過分析數(shù)據(jù)樣本在高維空間內(nèi)的分布情況,可以找出數(shù)據(jù)在高維空間內(nèi)的主要變動方向和趨勢,從而提取出包含有原始數(shù)據(jù)絕大部信息的特征向量來代替高維的、高相關(guān)性的原始數(shù)據(jù)。在以PCA技術(shù)作為數(shù)據(jù)預(yù)處理算法的基礎(chǔ)上,本文先后提出了基于PCA技術(shù)結(jié)合徑向基函數(shù)(Radical Basis Function, RBF)神經(jīng)網(wǎng)絡(luò)的空氣壓縮機故障診斷方法和基于PCA技術(shù)結(jié)合D-S證據(jù)理論的空氣壓縮機故障診斷方法。 基于PCA技術(shù)與RBF神經(jīng)網(wǎng)絡(luò)的空氣壓縮機故障診斷方法,是通過建立空氣壓縮機運行狀態(tài)的主元模型來處理采集到的龐大、高相關(guān)的原始數(shù)據(jù)集,利用特征提取的方法簡化原始數(shù)據(jù),并使用簡化后的樣本數(shù)據(jù)訓(xùn)練RBF神經(jīng)網(wǎng)絡(luò),最后通過訓(xùn)練好的RBF識別網(wǎng)絡(luò)實現(xiàn)空氣壓縮機的故障分類。該方法可以充分發(fā)揮PCA技術(shù)在數(shù)據(jù)降維、除相關(guān)性上的優(yōu)勢,極大的簡化復(fù)雜的檢測數(shù)據(jù)。同時,PCA的降維作用也為RBF網(wǎng)絡(luò)的訓(xùn)練和識別減化了大量的運算過程,從而可以提高神經(jīng)網(wǎng)絡(luò)訓(xùn)練和識別的速度,同時神經(jīng)網(wǎng)絡(luò)處理數(shù)據(jù)維數(shù)的降低,不僅避免了RBF網(wǎng)絡(luò)在訓(xùn)練過程中由于處理數(shù)據(jù)維數(shù)過高可能發(fā)生崩潰的危險,而且還提高了神經(jīng)網(wǎng)絡(luò)的分辨率。 基于PCA技術(shù)與D-S證據(jù)理論的空氣壓縮機故障診斷方法,是一種基于信息融合思想的故障診斷方法,該方法從空氣壓縮機不同運行狀態(tài)的角度(即不同證據(jù))進行觀察,通過分析檢測數(shù)據(jù)在各證據(jù)下呈現(xiàn)的特征信息對壓縮機的運行狀態(tài)進行判斷,最后以D-S組合規(guī)則將各證據(jù)下的判別結(jié)果融合成一個綜合的結(jié)果,從而實現(xiàn)空氣壓縮機運行狀態(tài)的最終判別。該方法可以更加全面分析檢測數(shù)據(jù)的信息,具有較快的處理速度和抗干擾能力強的特點,可以實現(xiàn)高精確度的故障分離和判別。
[Abstract]:The purpose of this paper is to find an efficient and feasible fault diagnosis method for air compressor. Based on the working principle, mechanical structure, fault type and fault mechanism of the air compressor, the main fault characteristics of the air compressor are summarized, and the realization requirements of the fault diagnosis system for the air compressor are summarized. In order to find a suitable fault diagnosis algorithm, this paper focuses on how to solve the problem of high correlation between the air compressor fault detection variables and the detection variables. In order to solve this problem, the principal component analysis (Principal Components Analysis, PCA) technique is used as the preprocessing algorithm of the detection data in this paper. By analyzing the distribution of data samples in the high-dimensional space, the algorithm can find out the main changing directions and trends of the data in the high-dimensional space, and then extract the feature vectors which contain most of the information of the original data to replace the high-dimensional ones. Highly correlated raw data. On the basis of the PCA technology as the data preprocessing algorithm, this paper proposes the combination of the radial basis function (Radical Basis Function, and the PCA technology. RBF) neural network based air compressor fault diagnosis method and air compressor fault diagnosis method based on PCA technology combined with DES evidence theory. The fault diagnosis method of air compressor based on PCA technology and RBF neural network is to process the large and highly correlated original data set by establishing the principal component model of the running state of air compressor. The method of feature extraction is used to simplify the original data, and the simplified sample data is used to train the RBF neural network. Finally, the trained RBF recognition network is used to realize the fault classification of the air compressor. This method can make full use of the advantages of PCA technology in data dimensionality reduction and correlation, and greatly simplify the complex detection data. At the same time, the dimensionality reduction of PCA reduces a lot of operation process for the training and recognition of RBF network, which can improve the speed of training and recognition of neural network, and reduce the dimension of data processed by neural network at the same time. The RBF network not only avoids the possibility of collapse due to the high dimension of processing data, but also improves the resolution of neural network in the process of training. The fault diagnosis method of air compressor based on PCA technology and D S evidence theory is a fault diagnosis method based on the idea of information fusion. The method is observed from the angle of different operating state of air compressor (that is, different evidence), and the fault diagnosis method is based on the idea of information fusion. By analyzing the characteristic information of the test data under each evidence, the running state of the compressor is judged. Finally, the discriminant results under each evidence are fused into a comprehensive result by the combination rule of DES. Thus, the final discrimination of the running state of the air compressor can be realized. This method can analyze the information of the detected data more comprehensively, has the characteristics of fast processing speed and strong anti-jamming ability, and can realize high-precision fault separation and discrimination.
【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3;TH45

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本文編號：2469373