本文選題：破碎機電動機　切入點：瞬時功率　出處：《西安科技大學(xué)》2017年碩士論文

【摘要】：在煤礦產(chǎn)業(yè)中,電動機作為驅(qū)動裝置被廣泛應(yīng)用于破碎機、篩分機、刮板機、掘進機等大型設(shè)備中,因此,對礦用破碎機電動機的運行狀態(tài)和故障類型進行及時檢修是非常重要的,不僅關(guān)乎整個煤礦開采系統(tǒng)的正常安全運行,更加關(guān)乎煤炭工作人員的人身安全。本文以礦用破碎機電動機故障中最為常見的電動機轉(zhuǎn)子斷條和偏心故障為例。當(dāng)?shù)V用電動機轉(zhuǎn)子產(chǎn)生故障時,在其定子兩端的線電流中會產(chǎn)生相對應(yīng)的故障特征頻率,將特征頻率變化反映到轉(zhuǎn)子電流特征量變化中,但由于轉(zhuǎn)子電流中產(chǎn)生的故障特征頻率會因礦用電動機的轉(zhuǎn)差率較小,其故障特征值被基波頻率淹沒。因此,求取礦用電動機其轉(zhuǎn)子兩端的線電流對應(yīng)的線電壓,進而求取礦用電動機轉(zhuǎn)子的瞬時功率,對礦用電動機轉(zhuǎn)子瞬時功率的率頻譜圖進行小波包分解、重構(gòu),從而對不同頻段的頻率特征量能量值進行提取,作為識別破碎機電動機轉(zhuǎn)子故障的特征量。本文在所提取的不同頻段礦用破碎機電動機轉(zhuǎn)子故障特征量中,有些故障特征量可能存在冗余、重復(fù)、不確定情況,這些特征量不僅影響診斷速度,更會降低故障診斷的準確性。所以,在對數(shù)據(jù)樣本進行故障診斷分類之前應(yīng)先利用粗糙集理論(RS)對特征量樣本進行預(yù)處理,從而減冗余數(shù)據(jù),獲得最終數(shù)據(jù)樣本。由于礦用破碎機電動機惡劣的工作環(huán)境,使數(shù)據(jù)不能大量提取,又為了提高故障診斷率而引入的粗糙集算法對特征量樣本進行預(yù)處理后,會使樣本數(shù)據(jù)減少,因此,本文應(yīng)用對小樣本、非線性數(shù)據(jù)進行較好地診斷分類的支持向量機(SVM)算法,實現(xiàn)對礦用電動機轉(zhuǎn)子故障的診斷與分類,使誤判率降低。仿真實驗結(jié)果表明,經(jīng)粗糙集處理后的故障特征量的診斷結(jié)果更加準確。
[Abstract]:In coal mine industry, motor as driving device is widely used in crusher, sieve machine, scraper, roadheader and other large equipment, therefore, It is very important to examine and repair the running state and fault type of the motor of mine crusher in time, which is not only related to the normal and safe operation of the whole coal mining system, This paper takes the breakage and eccentricity faults of motor rotor, which is the most common fault of mine crusher, as an example. The corresponding fault characteristic frequency will be produced in the line current at the two ends of the stator, and the change of the characteristic frequency will be reflected in the change of the rotor current characteristic quantity. However, the fault characteristic frequency generated in the rotor current will be due to the small slip rate of the mine motor. Therefore, the linear voltage corresponding to the line current at the two ends of the rotor of the mine motor is obtained, and then the instantaneous power of the rotor of the mine motor is obtained. The frequency spectrum of instantaneous power of mine motor rotor is decomposed and reconstructed by wavelet packet, and the energy value of frequency characteristic quantity is extracted from different frequency range. As the characteristic quantity to identify the rotor fault of the crusher motor, in this paper, some fault characteristics may exist redundancy, repetition and uncertainty in the different frequency range of the rotor fault characteristic of the mine crusher motor. These features not only affect the speed of diagnosis, but also reduce the accuracy of fault diagnosis. Therefore, the rough set theory should be used to preprocess the feature samples before classifying them, so as to reduce the redundant data. The final data sample is obtained. Because of the bad working environment of the motor of the crusher, the data can not be extracted in large quantities, and the rough set algorithm introduced in order to improve the fault diagnosis rate is used to preprocess the feature sample. Therefore, this paper applies the support vector machine (SVM) algorithm to diagnose and classify the rotor faults of mine motor by using the support vector machine (SVM) algorithm for the diagnosis and classification of small sample and nonlinear data. The simulation results show that the diagnosis results of the fault feature quantity treated by rough set are more accurate.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD607

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本文編號：1689242