本文選題：弱特征提取 + 早期故障診斷��； 參考：《哈爾濱工業(yè)大學(xué)》2011年碩士論文

【摘要】：工業(yè)自動(dòng)化水平的提高對(duì)設(shè)備提出了早期故障診斷的需求。由于復(fù)雜的工作環(huán)境和現(xiàn)場(chǎng)噪聲的干擾,設(shè)備的故障信號(hào)容易被噪聲污染,如何有效的從低信噪比監(jiān)測(cè)信號(hào)中提取弱故障特征成為故障診斷領(lǐng)域迫切需要解決的問(wèn)題。結(jié)合故障信號(hào)自身的特點(diǎn),運(yùn)用弱信號(hào)特征提取方法和早期故障診斷理論解決機(jī)械故障診斷中信號(hào)降噪和弱特征加強(qiáng)等問(wèn)題以及進(jìn)行設(shè)備的早期故障診斷是當(dāng)前機(jī)械故障診斷領(lǐng)域迫切需要研究的重要課題之一。本文研究了信號(hào)的降噪方法、設(shè)備故障的弱特征提取、早期故障診斷理論及其在軸承故障診斷中的應(yīng)用。 (1)采用小波分析的多分辨率算法,信號(hào)被分解為不同尺度上的低頻分量和高頻分量,信號(hào)中的微弱特征隨著尺度因子的遞進(jìn)變化逐漸被放大。在每個(gè)分解尺度上,將變換系數(shù)與通過(guò)閾值規(guī)則設(shè)置的門限因子進(jìn)行比較處理,實(shí)現(xiàn)了信號(hào)的降噪處理,提高了信噪比和加強(qiáng)了信號(hào)中弱特征。 (2)基于信號(hào)的局部特征分析,經(jīng)驗(yàn)?zāi)B(tài)分解采用包絡(luò)分析理論將故障信號(hào)分解為頻率從高到低變化的固有模態(tài)分量。針對(duì)經(jīng)驗(yàn)?zāi)B(tài)分解過(guò)程的邊界效應(yīng)問(wèn)題和傳統(tǒng)延拓方法的缺陷,本文采用了基于奇異值分解和支持向量回歸機(jī)的端點(diǎn)預(yù)測(cè)延拓方法。奇異譜分析方法能夠有效地檢測(cè)信號(hào)中的周期特征,為確定支持向量回歸機(jī)延拓點(diǎn)的數(shù)目提供了一種可行的方法。通過(guò)閾值掃描法剔除了分解產(chǎn)生的“偽”分量,分析表明該方法能夠有效地抑制邊界效應(yīng)和提取信號(hào)中的弱特征分量。 (3)針對(duì)設(shè)備早期故障特征不明顯、可分性差的特點(diǎn),采用支持向量機(jī)建立最優(yōu)分類決策模型。采用高斯核函數(shù)增加樣本的可分性,實(shí)現(xiàn)了故障特征空間的映射變換。本文以軸承故障為研究對(duì)象,系統(tǒng)地研究了故障類別、訓(xùn)練樣本數(shù)目和故障診斷精度之間的關(guān)系,分析表明該方法能夠有效地解決小樣本情況下故障診斷精度低的問(wèn)題。 (4)基于Matlab語(yǔ)言和VB環(huán)境的混合編程技術(shù)開(kāi)發(fā)了信號(hào)的特征提取和設(shè)備的早期故障診斷可視化操作系統(tǒng),提供了一個(gè)操作方便、可擴(kuò)展性強(qiáng)的特征提取和早期故障診斷平臺(tái)。
[Abstract]:The improvement of the level of industrial automation puts forward the requirement of early fault diagnosis for the equipment. Because of the complex working environment and the interference of the field noise, the fault signal of the equipment is liable to be polluted by noise. How to extract the weak fault feature from the low signal-to-noise ratio monitoring signal becomes an urgent problem to be solved in the field of fault diagnosis. Combined with the characteristics of the fault signal itself, The application of weak signal feature extraction method and early fault diagnosis theory to solve the problems of signal noise reduction and weak feature enhancement in mechanical fault diagnosis and the early fault diagnosis of equipment are urgently needed in the field of mechanical fault diagnosis. One of the important subjects of research. In this paper, the signal denoising method, the weak feature extraction of equipment fault, the theory of early fault diagnosis and its application in bearing fault diagnosis are studied. The signal is decomposed into low frequency component and high frequency component in different scales, and the weak feature of the signal is amplified gradually with the change of scale factor. 1) using the multi-resolution algorithm of wavelet analysis, the signal is decomposed into low-frequency and high-frequency components on different scales. In each decomposition scale, the transform coefficient is compared with the threshold factor set by the threshold rule, the signal noise reduction is realized, the signal-to-noise ratio (SNR) is improved and the weak feature in the signal is strengthened. 2) based on the local characteristic analysis of the signal, the empirical mode decomposition uses the envelope analysis theory to decompose the fault signal into the inherent modal component of the frequency change from high to low. Aiming at the boundary effect problem of empirical mode decomposition process and the defects of the traditional continuation method, the endpoint prediction continuation method based on singular value decomposition (SVD) and support vector regression machine (SVM) is used in this paper. The singular spectrum analysis method can effectively detect the periodic characteristics in the signal, which provides a feasible method for determining the number of extension points of the support vector regression machine. The "pseudo-component" component produced by decomposition is eliminated by threshold scanning method. The analysis shows that the method can effectively suppress the boundary effect and extract the weak characteristic component from the signal. 3) aiming at the characteristics that the early fault feature is not obvious and the separability is poor, support vector machine (SVM) is used to establish the optimal classification decision model. The Gao Si kernel function is used to increase the separability of the samples and the mapping transformation of fault feature space is realized. In this paper, the relationship among the fault types, the number of training samples and the fault diagnosis accuracy is systematically studied. The analysis shows that this method can effectively solve the problem of low fault diagnosis accuracy in the case of small samples. Based on the mixed programming technology of Matlab language and VB environment, the visual operating system of signal feature extraction and early fault diagnosis is developed, which provides a convenient and extensible platform for feature extraction and early fault diagnosis.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3

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本文編號(hào)：1972916