本文關(guān)鍵詞： 機(jī)械密封 狀態(tài)監(jiān)測(cè) 小波包 聲發(fā)射特征 Elman神經(jīng)網(wǎng)絡(luò) PSO　出處：《西南交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：機(jī)械密封是旋轉(zhuǎn)機(jī)械中最常用的軸封形式之一,因其可靠性高、泄漏量少、工作壽命長(zhǎng)和適用性強(qiáng)等特點(diǎn),被廣泛地用于石油化工、航天航空和核電能源等領(lǐng)域。因此,機(jī)械密封件的使用性能將直接對(duì)機(jī)械生產(chǎn)設(shè)備的安全性、生產(chǎn)過(guò)程的效率和生產(chǎn)成本產(chǎn)生影響。為此,對(duì)機(jī)械密封件采取實(shí)時(shí)狀態(tài)監(jiān)測(cè),獲取有用的特征信息,分析機(jī)械密封在啟動(dòng)過(guò)程中的端面摩擦狀態(tài),將是十分必要的。可以在密封件失效前及時(shí)發(fā)現(xiàn)并維修,避免因過(guò)早更換密封件造成的資源浪費(fèi)、成本提高,或是延遲更換造成的安全事故。通過(guò)機(jī)械密封開啟過(guò)程監(jiān)測(cè)實(shí)驗(yàn)的設(shè)計(jì),選取電渦流和聲發(fā)射法對(duì)密封端面的膜厚信息進(jìn)行監(jiān)測(cè)。實(shí)驗(yàn)采集了機(jī)械密封在開啟全過(guò)程中的電渦流和聲發(fā)射信號(hào),通過(guò)對(duì)電渦流信號(hào)的分析,建立起信號(hào)的變化與端面接觸狀態(tài)和摩擦狀態(tài)改變的對(duì)應(yīng)關(guān)系。將密封的開啟過(guò)程分為干摩擦、混合摩擦和流體摩擦三種摩擦狀態(tài),采集的信號(hào)在這三個(gè)狀態(tài)中均有明顯的特征體現(xiàn)。然后對(duì)開啟過(guò)程中的聲發(fā)射信號(hào)進(jìn)行分析,聲發(fā)射信號(hào)的變化情況也能與三種摩擦狀態(tài)進(jìn)行對(duì)應(yīng),采用小波包分析法對(duì)信號(hào)進(jìn)行降噪處理。在設(shè)計(jì)的有無(wú)密封環(huán)對(duì)比實(shí)驗(yàn)中,分析得到中高頻信號(hào)包含更多的與機(jī)械密封有關(guān)的信息的結(jié)論,選取聲發(fā)射信號(hào)中的高頻信息,根據(jù)機(jī)械密封端面的摩擦特性選擇適合的特征指標(biāo),進(jìn)行時(shí)頻域特征提取,篩選得到有效的聲發(fā)射信號(hào)特征。篩選得到的特征進(jìn)行歸一化處理后,分析特征對(duì)三種摩擦狀態(tài)具有較好的可識(shí)別性。將歸一化后的特征值作為Elman神經(jīng)網(wǎng)絡(luò)的輸入向量,構(gòu)建含反饋層的四層網(wǎng)絡(luò)模型。利用訓(xùn)練樣本進(jìn)行訓(xùn)練后,對(duì)測(cè)試樣本進(jìn)行識(shí)別,得到較好的識(shí)別效果。之后選取不同的訓(xùn)練樣本和測(cè)試樣本,建立不同的網(wǎng)絡(luò)模型進(jìn)行模式識(shí)別,發(fā)現(xiàn)均能很好地將不同的摩擦狀態(tài)數(shù)據(jù)進(jìn)行分類。結(jié)果證明,選取的聲發(fā)射特征能有效地識(shí)別機(jī)械密封開啟過(guò)程中的端面情況�；赑SO算法實(shí)現(xiàn)對(duì)神經(jīng)網(wǎng)絡(luò)的優(yōu)化,通過(guò)加慣性權(quán)重因子和矩陣化設(shè)計(jì)實(shí)現(xiàn)了對(duì)PSO算法的改進(jìn),提高了算法的運(yùn)行效率同時(shí)提升了算法的收斂速度。對(duì)比優(yōu)化前后神經(jīng)網(wǎng)絡(luò)的輸出結(jié)果證實(shí),PSO算法對(duì)神經(jīng)網(wǎng)絡(luò)的訓(xùn)練速度和精度、收斂速度和狀態(tài)識(shí)別精度等方面,均有明顯的優(yōu)化效果。
[Abstract]:Mechanical seal is one of the most commonly used shaft seals in rotating machinery, because of its high reliability, less leakage, long working life and strong applicability, it is widely used in petrochemical, aerospace and nuclear energy fields. The performance of mechanical seals will have a direct impact on the safety of mechanical production equipment, the efficiency of production process and production cost. Therefore, real-time monitoring of mechanical seals is adopted to obtain useful feature information. It will be very necessary to analyze the friction state of the end face of the mechanical seal in the starting process. It can be found and repaired in time before the failure of the seal, so as to avoid the waste of resources and increase the cost caused by the premature replacement of the seal. Or a safety accident caused by a delay in replacement. The design of the monitoring experiment through the mechanical seal opening process, Eddy current and acoustic emission methods are selected to monitor the film thickness information of the seal end face. The eddy current and acoustic emission signals of the mechanical seal during the whole process of opening are collected experimentally, and the eddy current signal is analyzed. The relationship between the signal change and the change of the contact state and the friction state is established. The opening process of the seal is divided into three kinds of friction states: dry friction, mixed friction and fluid friction. The collected signals have obvious characteristics in these three states. Then the acoustic emission signals in the process of opening are analyzed, and the changes of the acoustic emission signals can also correspond to the three friction states. The wavelet packet analysis method is used to reduce the noise of the signal. In the contrast experiment with or without the seal ring, the conclusion that the middle and high frequency signal contains more information related to mechanical seal is obtained, and the high frequency information of the acoustic emission signal is selected. According to the friction characteristics of the mechanical seal face, the suitable feature index is selected, and the feature extraction in time-frequency domain is carried out, and the effective acoustic emission signal feature is obtained. After normalized processing, the selected features are normalized. The normalized eigenvalue is used as the input vector of Elman neural network, and a four-layer network model with feedback layer is constructed. After different training samples and test samples are selected, different network models are established for pattern recognition. The results show that the selected acoustic emission features can effectively identify the end face in the process of mechanical seal opening. The neural network is optimized based on PSO algorithm. The improvement of PSO algorithm is realized by adding inertia weight factor and matrix design. Compared with the output results of neural network before and after optimization, the training speed and precision, convergence speed and state recognition accuracy of PSO algorithm for neural network are proved. All of them have obvious optimization effect.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TH136

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