本文選題：小波分析　切入點(diǎn)：模式提取　出處：《北方工業(yè)大學(xué)》2012年碩士論文

【摘要】：先進(jìn)的故障診斷技術(shù)是設(shè)備發(fā)展的前提,沒有先進(jìn)的故障診斷技術(shù)設(shè)備發(fā)展就無從談起。故障診斷技術(shù)不僅為設(shè)備發(fā)展提供動力,也是設(shè)備平穩(wěn)運(yùn)行的保障,避免設(shè)備故障帶來的損失。在故障診斷技術(shù)中,故障特征提取至關(guān)重要,小波分析是一種新型的故障提取技術(shù)。 小波分析技術(shù)在高維數(shù)據(jù)特征模式提取中有著廣泛的應(yīng)用。它不僅能有效降低數(shù)據(jù)維度,還能在時域和頻域?qū)π盘枖?shù)據(jù)進(jìn)行分解,提取出信號中的激勵特征。但小波分析在故障模式提取應(yīng)用中有四方面配置選擇問題：小波基函數(shù)選擇、小波分解層數(shù)、小波系數(shù)選取和特征向量生成算法選用。這四個方面問題影響著小波分析提取出的故障模式的優(yōu)劣,并制約了電路板故障智能診斷的正確率。本文通過對小波的研究,提出了小波分析在故障模式提取中的評價標(biāo)準(zhǔn),以便于小波分析史好發(fā)揮其特性。 小波分析作為一種新型的信號分解工具,能根據(jù)時域和頻域的需求,拉遠(yuǎn)或拉近“顯微鏡”鏡頭,達(dá)到提取信號故障特征的目的,因此小波分析更適合提取信號低頻中的輪廓信息和高頻中的奇異信號特征。論文介紹了能量、極大值、小波熵三種特征提取算法和BP神經(jīng)網(wǎng)絡(luò)、支持向量機(jī)兩種模式識別算法,以及信息融合技術(shù)。并在此基礎(chǔ)上提出了波動性函數(shù)、信噪比、時間復(fù)雜度、診斷正確率等基于小波分析的故障模式提取評價標(biāo)準(zhǔn)。 最后選用與或邏輯輸出控制電路作為測試電路,采集信號數(shù)據(jù)。測試中,根據(jù)電路的工作原理和常見故障類型,選取25個采樣節(jié)點(diǎn)對38種電路狀態(tài)分三次采集信號波形數(shù)據(jù)。然后,以小波分析作特征提取算法,以神經(jīng)網(wǎng)絡(luò)和和支持向量機(jī)作分類器,驗證了提出的小波分析配置評價標(biāo)準(zhǔn)是有效的。
[Abstract]:Advanced fault diagnosis technology is the premise of equipment development. Without advanced fault diagnosis technology, there can be no development of equipment. Fault diagnosis technology not only provides power for equipment development, but also guarantees the smooth operation of equipment. In fault diagnosis, fault feature extraction is very important. Wavelet analysis is a new fault extraction technology. Wavelet analysis is widely used in feature pattern extraction of high-dimensional data. It can not only reduce the dimension of data, but also decompose the signal data in time and frequency domain. But in the application of wavelet analysis in fault mode extraction, there are four problems in configuration selection: wavelet basis function selection, wavelet decomposition layer number, The selection of wavelet coefficients and the selection of feature vector generation algorithm affect the advantages and disadvantages of the fault mode extracted by wavelet analysis, and restrict the correct rate of fault intelligent diagnosis of circuit board. The evaluation criteria of wavelet analysis in fault mode extraction are put forward in order to give full play to the characteristics of wavelet analysis history. As a new signal decomposing tool, wavelet analysis can draw far or close the lens of "microscope" according to the demand of time domain and frequency domain, so as to extract the fault feature of signal. Therefore, wavelet analysis is more suitable for extracting contour information from low frequency signal and singular signal feature from high frequency. This paper introduces three feature extraction algorithms: energy, maximum, wavelet entropy, BP neural network and support vector machine. On the basis of this, the evaluation criteria of fault pattern extraction based on wavelet analysis, such as volatility function, signal-to-noise ratio, time complexity and diagnostic accuracy, are proposed. Finally, the control circuit of and or logic output is selected as the test circuit to collect the signal data. In the test, according to the working principle of the circuit and the common fault type, Twenty-five sampling nodes are selected to collect the signal waveform data three times for 38 circuit states. Then, wavelet analysis is used as feature extraction algorithm, neural network and support vector machine are used as classifiers. It is verified that the proposed wavelet analysis configuration evaluation criteria are effective.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3

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