需要金幣： 1000 個(gè)金幣 |
資料包括：完整論文 |  |
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| 轉(zhuǎn)換比率：金額 X 10=金幣數(shù)量， 例100元=1000金幣 | 論文字?jǐn)?shù)：8606 |  | |
| 折扣與優(yōu)惠：團(tuán)購(gòu)最低可5折優(yōu)惠 - 了解詳情 | 論文格式：Word格式(*.doc) |  |
摘要:對(duì)電機(jī)常見故障的在線診斷和分析，不僅可以及早地發(fā)現(xiàn)故障和預(yù)防故障的進(jìn)一步惡化，減少突發(fā)事故造成的停產(chǎn)損失，防止對(duì)人員和設(shè)備安全的威脅，并為實(shí)現(xiàn)狀態(tài)檢修創(chuàng)造條件；而且能為設(shè)計(jì)制造者提供經(jīng)驗(yàn)，積累數(shù)據(jù)，有利于電機(jī)性能及可靠性的改進(jìn)；同時(shí)對(duì)于電機(jī)故障的定位、類型決策及其維修等都是極其重要的。 論文在對(duì)電機(jī)常見故障類型及故障發(fā)生的機(jī)理進(jìn)行詳細(xì)分析的基礎(chǔ)上，提出了基于BP和RBF神經(jīng)網(wǎng)絡(luò)的電機(jī)故障診斷方案。主要內(nèi)容包括： 論文首先詳細(xì)分析了電機(jī)常見故障的類型，研究了包括軸承故障、轉(zhuǎn)子偏心故障、電刷故障、電樞故障等幾種常見故障的產(chǎn)生機(jī)理及特征。 然后在分析了BP神經(jīng)網(wǎng)絡(luò)和RBF神經(jīng)網(wǎng)絡(luò)的基本原理的基礎(chǔ)上，討論了神經(jīng)網(wǎng)絡(luò)應(yīng)用于電機(jī)故障診斷的可行性。由于電機(jī)發(fā)生故障時(shí)，定子電流的幅值在其相對(duì)應(yīng)的特征頻率上的會(huì)出現(xiàn)十分明顯的增加，而且這些頻率點(diǎn)上的幅值所增加的大小是與故障的嚴(yán)重程度成正比。利用這一信號(hào)特征，我們就可以對(duì)故障進(jìn)行判斷和分類。 最后，利用上述信號(hào)特征，論文分別提出了基于BP神經(jīng)網(wǎng)絡(luò)算法和基于RBF神經(jīng)網(wǎng)絡(luò)算法的電機(jī)故障診斷方案，并通過實(shí)驗(yàn)驗(yàn)證了該算法的正確性。 關(guān)鍵詞：神經(jīng)網(wǎng)絡(luò)；電機(jī)故障；診斷
Abstract:Common motor online fault diagnosis and analysis, not only for early failure and the further deterioration of the prevention of failure, reducing the cut-off losses caused by unexpected incidents, to prevent a threat to the safety of personnel and equipment, and create conditions for realization of state maintenance; but also to provide experience for the design of the manufacturer, the accumulation of data, is conducive to the improvement of motor performance and reliability; for the positioning of the motor failure, types of decision-making and its maintenance are extremely important. On the basis of the analysis of the mechanism of motor common type fault and failure, the motor fault diagnosis based on BP and RBF neural network is researched. The main contents include: Firstly, the common type failure of motor is analyzed, and the generation mechanism and characteristics of several common failures, such as rotor eccentricity fault, brush failure, armature failure, are researched. And then on the basis of analysis of the basic principle of BP neural network and RBF neural network to discuss the feasibility of neural network used in motor fault diagnosis. Due to motor failure, the stator current amplitude will appear in its characteristic frequency corresponding to the apparent increase in the size of the increase in the amplitude of these frequency points is proportional to the severity o f the fault. The characteristics of this signal, we can judge and classify the fault. Finally, using the above signal characteristics, the methods based on BP neural network algorithm and RBF neural network algorithm for motor fault diagnosis are introduced, and the correctness of the algorithm is verified by experiments. Key words: Neural network；Motor fault；Diagnosis
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