本文選題：高壓異步電機(jī)　切入點(diǎn)：電機(jī)轉(zhuǎn)子故障　出處：《長沙理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代科學(xué)技術(shù)的進(jìn)步和電力系統(tǒng)規(guī)模日趨龐大,電機(jī)在現(xiàn)代工業(yè)生產(chǎn)中充當(dāng)著越來越重要的角色。電機(jī)故障不僅會(huì)損壞電機(jī)本身,影響整個(gè)系統(tǒng)的正常運(yùn)轉(zhuǎn),嚴(yán)重的情況下甚至危及人身安全,造成巨大的經(jīng)濟(jì)損失和重大的社會(huì)影響,因此對(duì)電機(jī)故障的診斷具有重要意義和工程實(shí)用價(jià)值。研究感應(yīng)電機(jī)的監(jiān)測(cè)和故障診斷技術(shù),對(duì)預(yù)防感應(yīng)電機(jī)故障的發(fā)生,及時(shí)發(fā)現(xiàn)并消除故障,保證感應(yīng)電機(jī)可靠運(yùn)行,提高生產(chǎn)效率都具有十分重要的意義。論文在闡敘國內(nèi)外電機(jī)故障診斷研究現(xiàn)狀的基礎(chǔ)上,對(duì)高壓異步電機(jī)轉(zhuǎn)子的基本結(jié)構(gòu)和轉(zhuǎn)子故障進(jìn)行了簡(jiǎn)單的分析。在此基礎(chǔ)上提出了兩種基于不同故障診斷機(jī)理的電機(jī)轉(zhuǎn)子故障診斷方法：“質(zhì)樸型-貝葉斯網(wǎng)絡(luò)拓?fù)淠Ｐ汀焙汀靶〔ㄉ窠?jīng)網(wǎng)絡(luò)的故障診斷網(wǎng)絡(luò)模型”。由于電機(jī)系統(tǒng)故障信息中存在許多不確定性,電機(jī)轉(zhuǎn)子出現(xiàn)故障時(shí),在傳統(tǒng)方法的基礎(chǔ)上,通過綜合樣本信息和先驗(yàn)信息,建立基于轉(zhuǎn)子系統(tǒng)故障類型和對(duì)應(yīng)的故障征兆的貝葉斯網(wǎng)絡(luò)模型。貝葉斯網(wǎng)絡(luò)作為目前推理領(lǐng)域和不確定性知識(shí)表達(dá)的的有效模型之一,能高效的推理和表達(dá)不確定性知識(shí)和概率推理。同時(shí),電機(jī)的振動(dòng)故障信往往包含大量的短時(shí)突發(fā)、時(shí)變的成分,而傅里葉變換采用的方法是將信號(hào)從頻域和時(shí)域整體角度出發(fā),缺乏時(shí)頻局部性,不能準(zhǔn)確的對(duì)這些非平穩(wěn)隨機(jī)信號(hào)分析,故達(dá)不到故障信號(hào)特征提取的要求。小波神經(jīng)網(wǎng)絡(luò)就是在研究電機(jī)振動(dòng)信號(hào)的基礎(chǔ)上建立的故障診斷模型。采用小波時(shí)頻分析技術(shù)對(duì)電機(jī)故障信號(hào)消噪濾波并提取故障特征,然后用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行故障識(shí)別,最終達(dá)到故障診斷的目的。最后對(duì)異步電機(jī)典型故障轉(zhuǎn)子不對(duì)中、轉(zhuǎn)子質(zhì)量不平衡和軸承摩擦等故障用上面提到的兩種不同的診斷方法建立模型進(jìn)行故障診斷。結(jié)果證明了論文所設(shè)計(jì)的兩種方法能夠?qū)Ω邏寒惒诫姍C(jī)的故障有效地進(jìn)行診斷,提高了電機(jī)故障診斷的準(zhǔn)確性。
[Abstract]:With the progress of modern science and technology and the increasingly large scale of power system, motor plays an increasingly important role in modern industrial production. Motor failure will not only damage the motor itself, but also affect the normal operation of the whole system. Under serious circumstances, even endangering personal safety, causing huge economic loss and great social impact, it is of great significance and practical engineering value to diagnose motor fault. The monitoring and fault diagnosis technology of induction motor is studied. It is of great significance to prevent the occurrence of the fault of induction motor, to find and eliminate the fault in time, to ensure the reliable operation of the induction motor and to improve the production efficiency. The basic structure of rotor and rotor fault of HV asynchronous motor are simply analyzed. On the basis of this, two methods of rotor fault diagnosis based on different fault diagnosis mechanisms are proposed: "simple Bayesian network" The fault diagnosis network model of wavelet neural network. Because there are many uncertainties in the fault information of motor system, When the motor rotor is in trouble, on the basis of the traditional method, by synthesizing the sample information and the prior information, A Bayesian network model based on rotor system fault type and corresponding fault symptoms is established. Bayesian network is one of the effective models of reasoning field and uncertain knowledge representation. It can efficiently infer and express uncertain knowledge and probabilistic reasoning. At the same time, the vibration fault letter of motor often contains a large number of short-time burst and time-varying components. The method of Fourier transform is to analyze these non-stationary random signals from the whole angle of frequency domain and time domain without time-frequency localization. Wavelet neural network is a fault diagnosis model established on the basis of studying motor vibration signal. Wavelet time-frequency analysis technology is used to filter and extract fault feature of motor fault signal. Then BP neural network is used to identify the fault, and finally the purpose of fault diagnosis is achieved. Finally, the typical fault rotor of asynchronous motor is misaligned. The fault diagnosis of rotor mass imbalance and bearing friction is based on two different diagnosis methods mentioned above. The results show that the two methods designed in this paper can effectively diagnose the faults of high voltage asynchronous motor. The accuracy of motor fault diagnosis is improved.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM343

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本文編號(hào)：1616699