【摘要】：在旋轉(zhuǎn)機(jī)械設(shè)備狀態(tài)監(jiān)測和故障診斷研究中,故障的特征提取和模式識別關(guān)系到故障診斷的可靠性和準(zhǔn)確性,也是旋轉(zhuǎn)機(jī)械故障診斷研究中的關(guān)鍵問題。利用轉(zhuǎn)子振動(dòng)信號對其進(jìn)行狀態(tài)監(jiān)測和診斷是目前旋轉(zhuǎn)機(jī)械故障監(jiān)測和診斷研究中常用的方法。本論文以豐富和提高機(jī)械故障診斷理論與方法為目的,用現(xiàn)代信號處理技術(shù)中的盲信號分離方法為工具,以機(jī)械設(shè)備中應(yīng)用最廣泛的旋轉(zhuǎn)機(jī)械設(shè)備為研究對象,利用盲信號分離算法、中值濾波和盲信號分離相結(jié)合的方法、自適應(yīng)粒子群優(yōu)化的盲信號分離方法、降噪源分離方法等信號處理方法,對轉(zhuǎn)子系統(tǒng)故障特征提取問題開展了研究工作。具體研究內(nèi)容如下： (1)針對噪聲干擾下的旋轉(zhuǎn)機(jī)械故障特征提取問題,提出一種基于二階盲辨識的去除干擾噪聲方法。該方法利用旋轉(zhuǎn)機(jī)械振動(dòng)信號的非平穩(wěn)性特征,將采集到的信號分成不重疊的時(shí)間窗,然后對每個(gè)時(shí)間窗內(nèi)的時(shí)滯方差平均值進(jìn)行估計(jì),從而實(shí)現(xiàn)噪聲信號與源信號的分離。這里將盲信號分離理論應(yīng)用于消噪處理,其關(guān)鍵是分離噪聲,而不是濾除噪聲,因此在分離噪聲時(shí)不丟失有效信號,為消噪處理提供了一種新方法。此方法通過仿真和對實(shí)際轉(zhuǎn)子振動(dòng)數(shù)據(jù)的處理表明,該算法可有效地分離出干擾噪聲,提高采樣信號的準(zhǔn)確性。 (2)針對非線性機(jī)械故障信號分離依賴于非線性函數(shù)的選取問題,提出一種基于自適應(yīng)粒子群優(yōu)化的機(jī)械故障特征提取方法。該方法將采樣信號的負(fù)熵做為目標(biāo)函數(shù),然后引入自適應(yīng)粒子群優(yōu)化的概念,通過信號的狀態(tài)自適應(yīng)的調(diào)整慣性因子,使其負(fù)熵最大化,從而實(shí)現(xiàn)各振源信號的有效分離。仿真和試驗(yàn)結(jié)果表明,該方法提高了分離信號的相關(guān)系數(shù),實(shí)現(xiàn)了各源信號的有效分離。 (3)提出了基于降噪源分離的旋轉(zhuǎn)機(jī)械故障特征提取方法。該方法是根據(jù)旋轉(zhuǎn)機(jī)械振動(dòng)信號的統(tǒng)計(jì)特征,構(gòu)造降噪函數(shù),依據(jù)降噪函數(shù)實(shí)現(xiàn)各分量的分離。在對仿真故障信號實(shí)驗(yàn)的基礎(chǔ)上,定量比較了四種降噪函數(shù)的性能,發(fā)現(xiàn)基于正切降噪函數(shù)的分離結(jié)果相似系數(shù)最好,更適于混疊故障信號的分離。將基于正切降噪函數(shù)的源分離方法應(yīng)用于旋轉(zhuǎn)機(jī)械故障特征提取中,分析結(jié)果表明,該方法很好地從轉(zhuǎn)子混疊振動(dòng)信號中分離出了轉(zhuǎn)子由碰摩故障引起的轉(zhuǎn)子不平衡和不對中故障。 (4)針對源信號分離算法對強(qiáng)脈沖噪聲環(huán)境下的混疊振動(dòng)信號分離的失效,構(gòu)建了一種基于中值濾波和盲信號分離算法相結(jié)合的方法。該方法首先通過中值濾波降噪方法對振動(dòng)信號進(jìn)行降噪處理,然后通過盲信號分離算法對降噪后的混疊信號進(jìn)行分離。仿真和實(shí)驗(yàn)結(jié)果表明：在強(qiáng)脈沖噪聲干擾下,若直接采用盲信號分離算法進(jìn)行分離,其分離效果并不理想,若利用中值消噪和盲信號分離算法相結(jié)合的方法,則分離效果得到明顯提升。
[Abstract]:In the research of condition monitoring and fault diagnosis of rotating machinery, fault feature extraction and pattern recognition are related to the reliability and accuracy of fault diagnosis, and are also the key problems in the research of rotating machinery fault diagnosis. It is a common method to monitor and diagnose the rotor vibration signal in the fault monitoring and diagnosis of rotating machinery at present. The purpose of this paper is to enrich and improve the theory and method of mechanical fault diagnosis, to use the blind signal separation method in modern signal processing technology as a tool, and to study the most widely used rotating machinery in mechanical equipment. Using blind signal separation algorithm, median filter and blind signal separation method, adaptive particle swarm optimization blind signal separation method, noise reduction source separation method and other signal processing methods, The fault feature extraction of rotor system is studied. The main contents are as follows: (1) aiming at the problem of fault feature extraction of rotating machinery under noise interference, a noise removal method based on second-order blind identification is proposed. Based on the non-stationary characteristic of vibration signals of rotating machinery, the collected signals are divided into non-overlapping time windows, and then the time-delay mean variance in each time window is estimated, so that the noise signal is separated from the source signal. In this paper, the blind signal separation theory is applied to de-noise processing. The key point is to separate noise, not to filter noise. Therefore, effective signals are not lost when noise is separated, which provides a new method for de-noising processing. The simulation and processing of the actual rotor vibration data show that the proposed method can effectively separate out the interference noise and improve the accuracy of the sampling signal. (2) aiming at the problem that the separation of nonlinear mechanical fault signals depends on the nonlinear function, an adaptive particle swarm optimization (APSO) based method for mechanical fault feature extraction is proposed. In this method, the negative entropy of the sampled signal is taken as the objective function, and then the concept of adaptive particle swarm optimization is introduced. The state of the signal adjusts the inertia factor adaptively to maximize the negative entropy of the signal so as to realize the effective separation of the signals. The simulation and experimental results show that the method improves the correlation coefficient of the separation signal and realizes the effective separation of each source signal. (3) A fault feature extraction method for rotating machinery based on noise reduction source separation is proposed. According to the statistical characteristics of vibration signals of rotating machinery, the noise reduction function is constructed, and the separation of components is realized according to the noise reduction function. On the basis of simulation fault signal experiments, the performance of four noise reduction functions is quantitatively compared. It is found that the separation result based on tangent denoising function has the best similarity coefficient and is more suitable for separating aliasing fault signals. The source separation method based on tangent denoising function is applied to the fault feature extraction of rotating machinery. The analysis results show that, The rotor unbalance and misalignment caused by rub-impact fault are well separated from the rotor mixed vibration signal by this method. (4) in view of the failure of source signal separation algorithm for aliasing vibration signal separation under strong impulse noise, a method based on median filter and blind signal separation algorithm is proposed. Firstly, the vibration signal is de-noised by median filtering method, and then the aliasing signal is separated by blind signal separation algorithm. The simulation and experimental results show that the separation effect is not satisfactory if the blind signal separation algorithm is used directly under the strong impulse noise interference, and if the median de-noising algorithm is combined with the blind signal separation algorithm, The separation effect was obviously improved.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TH165.3

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