【摘要】：旋轉(zhuǎn)機(jī)械廣泛應(yīng)用于機(jī)械、冶金、電力、化工等行業(yè)。如果旋轉(zhuǎn)機(jī)械發(fā)生故障而未及時(shí)控制和消除，則可能導(dǎo)致設(shè)備損壞，不僅造成巨大的經(jīng)濟(jì)損失，甚至?xí)＜叭松戆踩�，，后果極為嚴(yán)重，因此對(duì)于旋轉(zhuǎn)機(jī)械的故障診斷具有重要的意義。由于轉(zhuǎn)子的軸心軌跡可以反映轉(zhuǎn)子故障類型，本課題從軸心軌跡入手研究轉(zhuǎn)子故障的自動(dòng)診斷方法。具體內(nèi)容包括： (1)軸心軌跡提純：利用小波方法對(duì)軸心軌跡進(jìn)行提純。首先對(duì)比了選用不同的小波函數(shù)以及分解到不同的層數(shù)時(shí)提純效果的差別，并選取出最佳的小波基和分解層數(shù)；然后根據(jù)轉(zhuǎn)子振動(dòng)信號(hào)的特點(diǎn)，分析了采樣頻率和轉(zhuǎn)速對(duì)提純效果的影響，并采用對(duì)信號(hào)重采樣的方法降低這種影響，保證提純效果； (2)軸心軌跡特征提�。涸谳S心軌跡提純時(shí)重采樣的基礎(chǔ)上進(jìn)行下采樣，計(jì)算軸心軌跡在各個(gè)旋轉(zhuǎn)周期內(nèi)的極徑（軸心軌跡上各點(diǎn)到基點(diǎn)O的距離）并對(duì)其進(jìn)行平移、伸縮、旋轉(zhuǎn)歸一化，采用歸一化的極徑序列作為軸心軌跡的特征。通過仿真試驗(yàn)驗(yàn)證了歸一化的極徑序列對(duì)于不同軸心軌跡具有不同的變化特征，而且下采樣后的極徑序列在每個(gè)旋轉(zhuǎn)周期有相同的樣本長度，便于下一步識(shí)別； (3)軸心軌跡的識(shí)別：采用軸心軌跡的極徑序列作為樣本，討論了建立相應(yīng)的BP神經(jīng)網(wǎng)絡(luò)的過程，并用其對(duì)樣本進(jìn)行訓(xùn)練和識(shí)別，結(jié)果表明該網(wǎng)絡(luò)能有效區(qū)分不同類型的軸心軌跡； (4)試驗(yàn)驗(yàn)證：在轉(zhuǎn)子-軸承試驗(yàn)臺(tái)上，測得幾組實(shí)際故障的軸心軌跡，并對(duì)實(shí)測信號(hào)進(jìn)行提純、特征提取和識(shí)別，結(jié)果表明本文的方法是有效的。
[Abstract]:Rotating machinery is widely used in machinery, metallurgy, power, chemical and other industries. If the failure of rotating machinery is not controlled and eliminated in time, it may lead to equipment damage, which may not only cause huge economic losses, but even endanger personal safety, and the consequences are very serious, so it is of great significance for the fault diagnosis of rotating machinery. Because the axial trajectory of rotor can reflect the type of rotor fault, the automatic diagnosis method of rotor fault is studied in this paper. The main contents are as follows: (1) Axis trajectory purification: wavelet method is used to purify the axis trajectory. Firstly, the difference of purification effect when different wavelet functions are selected and decomposed into different layers is compared, and the best wavelet basis and decomposition layer number are selected. Then, according to the characteristics of rotor vibration signal, the influence of sampling frequency and rotating speed on purification effect is analyzed, and the method of signal resampling is used to reduce this effect and ensure the purification effect. (2) feature extraction of axis trajectory: on the basis of resampling when the axis trajectory is purified, the polar diameter of the axis trajectory in each rotation cycle (the distance from each point on the axis trajectory to the base point O) is calculated, and its translation, expansion and rotation normalization are carried out. The normalized polar diameter sequence is used as the characteristics of the axis trajectory. The simulation results show that the normalized polar diameter sequence has different variation characteristics for different axis trajectories, and the undersampled polar diameter sequence has the same sample length in each rotation cycle, which is convenient for the next step identification. (3) Identification of axis trajectory: using the polar diameter sequence of axis trajectory as sample, the process of establishing corresponding BP neural network is discussed, and the samples are trained and identified. The results show that the network can effectively distinguish different types of axis trajectory. (4) the experimental results show that the axial center trajectories of several groups of actual faults are measured on the rotor-bearing test-bed, and the measured signals are purified, feature extraction and recognition are carried out. the results show that the method in this paper is effective.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

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本文編號(hào)：2512003