基于小波包和模糊神經(jīng)網(wǎng)絡(luò)電機故障診斷研究
發(fā)布時間：2018-09-19 13:00
【摘要】：異步電動機有著簡單的結(jié)構(gòu),在實際應(yīng)用中價格也并不昂貴,較其他產(chǎn)品運行相對穩(wěn)定,因此在日常生產(chǎn)生活中有著舉足輕重的作用。由于機械負載、工作環(huán)境等各方面的影響,電機出現(xiàn)故障使其運行不穩(wěn)定形成經(jīng)濟等方面損失,乃至危及人身安全。因此,人們在研究異步電機的同時投入了大量精力去研究其故障診斷。隨著計算機被廣泛的應(yīng)用到信號處理中,電機的故障診斷方法也在人們的不斷努力下取得了很大的突破。本文在大量的瀏覽中英文文獻的基礎(chǔ)上,將小波包與模糊RBF神經(jīng)網(wǎng)絡(luò)相結(jié)合,并運用遺傳算法進行優(yōu)化的方法,研究了異步電機故障的診斷方法。振動信號能很全面的反映出電機的運行狀態(tài),小波包分析具有很高的分辨率等特點,本文在小波包能量故障特征提取的基礎(chǔ)上主要研究了以下內(nèi)容:研究了信號的能量特征提取,選取經(jīng)驗上常用的幾種小波包基,分別用來對振動信號進行分析,將幅值與能量做除法,選取商值最大的db3,用其對信號進行三層小波包分解,即可得到能量特征向量,不同的振動信號究其根本是其振動能量的不同,振動信號通過小波包分解,各層信號系數(shù)功率譜也有所不同,根據(jù)系數(shù)功率譜判斷不同的故障。研究了小波包與模糊RBF神經(jīng)網(wǎng)絡(luò)相結(jié)合的異步電機故障診斷方法。該方法先利用小波包分析處理振動信號并提取能量特征向量分別作為訓練樣本和檢測樣本,將故障的程度看作一個模糊的概念并對輸入信號進行模糊化。隱含層節(jié)點數(shù)通過一些經(jīng)驗公式可以得到,并逐個進行訓練,選擇輸出誤差較小訓練時間短的節(jié)點數(shù)。由于網(wǎng)絡(luò)的參數(shù)不容易確定,將三種常用的基函數(shù)中心選取方法相比較最終選擇有監(jiān)督法并結(jié)合梯度下降法。用訓練樣本對該網(wǎng)絡(luò)訓練,完成后,將檢測樣本輸入對其做檢驗。研究了小波包與遺傳優(yōu)化模糊RBF神經(jīng)網(wǎng)絡(luò)相結(jié)合的異步電機故障診斷方法。為了解決梯度下降法選取基函數(shù)中心時容易陷入極小值的問題,將上面的算法與遺傳算法結(jié)合到一起,網(wǎng)絡(luò)結(jié)構(gòu)相同,隱含層節(jié)點數(shù)經(jīng)過選擇后取值比較少,在此基礎(chǔ)上運用了遺傳算法與梯度下降法優(yōu)化基函數(shù)中心等網(wǎng)絡(luò)參數(shù),避免了出現(xiàn)極小值,減少了隱含層節(jié)點數(shù),縮短了收斂時間,準確度也有所提升。
[Abstract]:Asynchronous motor has a simple structure and is not expensive in practical application. It is relatively stable compared with other products, so it plays an important role in daily production and life. Because of the influence of mechanical load, working environment and so on, the failure of motor makes its operation unstable and result in economic loss, even endangering personal safety. Therefore, people devote a lot of energy to the fault diagnosis of asynchronous motor at the same time. With the wide application of computer in signal processing, the fault diagnosis method of motor has made a great breakthrough with the continuous efforts of people. Based on a large amount of literature in Chinese and English, this paper combines wavelet packet with fuzzy RBF neural network, and studies the fault diagnosis method of asynchronous motor using genetic algorithm. The vibration signal can fully reflect the running state of the motor, and wavelet packet analysis has the characteristics of high resolution, etc. Based on the energy fault feature extraction of wavelet packet, the following contents are studied in this paper: the energy feature extraction of signal is studied, and several kinds of wavelet packet bases, which are commonly used in experience, are selected to analyze the vibration signal respectively. The amplitude and energy are divided and the db3, with the largest quotient is selected to decompose the signal into three layers of wavelet packet. The energy eigenvector can be obtained. The different vibration signal is based on the difference of its vibration energy, and the vibration signal is decomposed by wavelet packet. The power spectrum of signal coefficients varies from layer to layer, and different faults are judged according to the power spectrum of coefficients. The fault diagnosis method of asynchronous motor based on wavelet packet and fuzzy RBF neural network is studied. In this method, the vibration signal is processed by wavelet packet analysis and the energy eigenvector is extracted as the training sample and the detection sample respectively. The degree of fault is regarded as a fuzzy concept and the input signal is blurred. The number of hidden layer nodes can be obtained by some empirical formulas and trained one by one to select the number of nodes with smaller output error and shorter training time. Because the parameters of the network are not easy to determine, the three commonly used methods of selecting the basis function center are compared with the supervised method and the gradient descent method. The network is trained with training samples. The fault diagnosis method of asynchronous motor based on wavelet packet and genetic optimization fuzzy RBF neural network is studied. In order to solve the problem that the gradient descent method is easy to fall into the minimum value when selecting the center of the basis function, the above algorithm is combined with the genetic algorithm. The network structure is the same, and the number of hidden layer nodes is less after selection. On this basis, genetic algorithm and gradient descent method are used to optimize the network parameters such as the center of the basis function. The minimum value is avoided, the number of hidden layer nodes is reduced, the convergence time is shortened, and the accuracy is improved.
【學位授予單位】：東北石油大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM343;TP183
本文編號：2250198
[Abstract]:Asynchronous motor has a simple structure and is not expensive in practical application. It is relatively stable compared with other products, so it plays an important role in daily production and life. Because of the influence of mechanical load, working environment and so on, the failure of motor makes its operation unstable and result in economic loss, even endangering personal safety. Therefore, people devote a lot of energy to the fault diagnosis of asynchronous motor at the same time. With the wide application of computer in signal processing, the fault diagnosis method of motor has made a great breakthrough with the continuous efforts of people. Based on a large amount of literature in Chinese and English, this paper combines wavelet packet with fuzzy RBF neural network, and studies the fault diagnosis method of asynchronous motor using genetic algorithm. The vibration signal can fully reflect the running state of the motor, and wavelet packet analysis has the characteristics of high resolution, etc. Based on the energy fault feature extraction of wavelet packet, the following contents are studied in this paper: the energy feature extraction of signal is studied, and several kinds of wavelet packet bases, which are commonly used in experience, are selected to analyze the vibration signal respectively. The amplitude and energy are divided and the db3, with the largest quotient is selected to decompose the signal into three layers of wavelet packet. The energy eigenvector can be obtained. The different vibration signal is based on the difference of its vibration energy, and the vibration signal is decomposed by wavelet packet. The power spectrum of signal coefficients varies from layer to layer, and different faults are judged according to the power spectrum of coefficients. The fault diagnosis method of asynchronous motor based on wavelet packet and fuzzy RBF neural network is studied. In this method, the vibration signal is processed by wavelet packet analysis and the energy eigenvector is extracted as the training sample and the detection sample respectively. The degree of fault is regarded as a fuzzy concept and the input signal is blurred. The number of hidden layer nodes can be obtained by some empirical formulas and trained one by one to select the number of nodes with smaller output error and shorter training time. Because the parameters of the network are not easy to determine, the three commonly used methods of selecting the basis function center are compared with the supervised method and the gradient descent method. The network is trained with training samples. The fault diagnosis method of asynchronous motor based on wavelet packet and genetic optimization fuzzy RBF neural network is studied. In order to solve the problem that the gradient descent method is easy to fall into the minimum value when selecting the center of the basis function, the above algorithm is combined with the genetic algorithm. The network structure is the same, and the number of hidden layer nodes is less after selection. On this basis, genetic algorithm and gradient descent method are used to optimize the network parameters such as the center of the basis function. The minimum value is avoided, the number of hidden layer nodes is reduced, the convergence time is shortened, and the accuracy is improved.
【學位授予單位】：東北石油大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM343;TP183
【參考文獻】
相關(guān)期刊論文 前10條
1 曹志彤,陳宏平,E．Ritchie;基于混沌神經(jīng)網(wǎng)絡(luò)動態(tài)聯(lián)想記憶的電機故障診斷[J];電工技術(shù)學報;2000年03期
2 黃迪山;孫月明;童忠鈁;;希爾伯特變換在故障診斷中的應(yīng)用[J];中國紡織大學學報;1991年06期
3 劉勁，吳小辰，孫揚聲，陳德樹;電力系統(tǒng)靜態(tài)失穩(wěn)和周期振蕩的局部分叉分析[J];電力系統(tǒng)自動化;1995年12期
4 陳書旺;張喜英;;模糊理論在RBF神經(jīng)網(wǎng)絡(luò)中的應(yīng)用[J];電腦知識與技術(shù);2008年04期
5 李占鋒,韓芳芳,鄭德忠;基于BP神經(jīng)網(wǎng)絡(luò)的電機轉(zhuǎn)子故障診斷的研究[J];河北科技大學學報;2001年03期
6 趙林桂,詹渝明,陳明濤;基于傅里葉變換的異步電機轉(zhuǎn)子故障檢測[J];東北電力技術(shù);2004年07期
7 孫麗穎,屈丹,閆鈿;傅里葉變換與小波變換在信號故障診斷中的應(yīng)用[J];遼寧工學院學報;2005年03期
8 喬俊飛;韓紅桂;;RBF神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)動態(tài)優(yōu)化設(shè)計[J];自動化學報;2010年06期
9 李春華;榮明星;;基于小波包和改進BP神經(jīng)網(wǎng)絡(luò)算法的電機故障診斷[J];現(xiàn)代電子技術(shù);2013年15期
10 趙飛鵬;沈久珩;;機械設(shè)備的狀態(tài)監(jiān)測與故障診斷 第二講 振動信號分析[J];有色設(shè)備;1990年02期
，本文編號：2250198
本文鏈接：http://www.sikaile.net/kejilunwen/dianlilw/2250198.html
最近更新
教材專著
