本文選題：故障診斷 + 轉(zhuǎn)子系統(tǒng)��； 參考：《湖南大學(xué)》2011年碩士論文

【摘要】：轉(zhuǎn)子系統(tǒng)的故障診斷過程包括診斷信息的獲取、故障特征信息提取和狀態(tài)識別三部分。其中,故障特征提取是診斷的關(guān)鍵。本文將時頻分析的新方法——局部均值分解法(Local Mean Decomposition,簡稱LMD)應(yīng)用于轉(zhuǎn)子系統(tǒng)的故障診斷中。該方法的特點是可以獲得一系列瞬時頻率具有物理意義的PF(Product function,簡稱PF)分量。本文對LMD方法在轉(zhuǎn)子系統(tǒng)故障診斷中的應(yīng)用進行了研究。主要研究工作如下: 1.針對轉(zhuǎn)子系統(tǒng)故障振動信號的非平穩(wěn)特性,提出了一種基于LMD和神經(jīng)網(wǎng)絡(luò)相結(jié)合的故障診斷方法。該方法首先對信號進行LMD分解,將其分解為若干個PF分量之和,再選取包含主要故障信息的PF分量做進一步分析,從這些分量中提取時域統(tǒng)計量和能量等特征參數(shù)作為神經(jīng)網(wǎng)絡(luò)的輸入?yún)?shù)來識別轉(zhuǎn)子系統(tǒng)的故障類別。結(jié)果表明,基于LMD與神經(jīng)網(wǎng)絡(luò)的故障診斷方法能夠準(zhǔn)確、有效地對轉(zhuǎn)子系統(tǒng)的工作狀態(tài)和故障類型進行分類。 2.提出了基于LMD和AR模型相結(jié)合的轉(zhuǎn)子系統(tǒng)故障診斷方法。該方法先用LMD方法將轉(zhuǎn)子振動信號分解成若干個瞬時頻率具有物理意義的PF分量之和,然后對每一個PF分量建立AR模型,提取模型參數(shù)和殘差方差作為故障特征向量,并以此作為神經(jīng)網(wǎng)絡(luò)分類器的輸入來識別轉(zhuǎn)子的工作狀態(tài)和故障類型。與內(nèi)稟模態(tài)函數(shù)分解法(Empirical Mode Decomposition,簡稱EMD方法)的對比研究表明,這兩種方法均能有效地應(yīng)用于轉(zhuǎn)子系統(tǒng)的故障診斷。但LMD方法在信號分解方面體現(xiàn)了更大的優(yōu)勢。 3.針對LMD分解法的頻率混淆問題,提出了基于改進的LMD和奇異值分解法相結(jié)合的轉(zhuǎn)子系統(tǒng)故障診斷方法。該方法先用小波包分解法將轉(zhuǎn)子振動信號分解成若干個小波包分量,進一步對各小波包分量進行LMD分解,得到一系列PF分量,形成初始特征向量矩陣。然后對初始特征向量矩陣進行奇異值分解得到矩陣的奇異值,將其作為特征向量輸入神經(jīng)網(wǎng)絡(luò)來識別轉(zhuǎn)子系統(tǒng)的工作狀態(tài)和故障類型。實驗結(jié)果表明,該方法能有效的用于轉(zhuǎn)子系統(tǒng)故障診斷。 4.提出了基于改進的LMD和時頻熵相結(jié)合的轉(zhuǎn)子系統(tǒng)故障診斷方法,轉(zhuǎn)子系統(tǒng)振動信號進行小波-LMD分解后,能量分布在具有不同時間尺度的PF分量上,轉(zhuǎn)子系統(tǒng)的狀態(tài)不同,能量在不同的PF分量上的分布是不一致的,表現(xiàn)為時頻分布上的不同,基于改進LMD方法的時頻熵就是上述時頻分布的定量描述,通過實驗數(shù)據(jù)分析可知,基于改進的LMD的時頻熵對轉(zhuǎn)子故障類別十分敏感,可用于轉(zhuǎn)子系統(tǒng)故障診斷。
[Abstract]:The fault diagnosis process of rotor system includes three parts: obtaining diagnosis information, extracting fault feature information and identifying state. Fault feature extraction is the key to diagnosis. In this paper, the local mean decomposition method, a new time-frequency analysis method, is applied to the fault diagnosis of rotor system. The characteristic of this method is that a series of PF(Product function components with physical meaning can be obtained. In this paper, the application of LMD method in rotor system fault diagnosis is studied. The main work of the study is as follows: 1. A fault diagnosis method based on LMD and neural network is proposed for the non-stationary characteristics of rotor system fault vibration signal. The method firstly decomposes the signal into the sum of several PF components by LMD decomposition, and then selects the PF component which contains the main fault information for further analysis. The characteristic parameters such as time-domain statistics and energy are extracted from these components as input parameters of the neural network to identify the fault types of the rotor system. The results show that the fault diagnosis method based on LMD and neural network can classify the working state and fault types of rotor system accurately and effectively. 2. A fault diagnosis method for rotor system based on LMD and AR model is proposed. Firstly, the rotor vibration signal is decomposed into the sum of PF components with physical meaning by LMD method, then AR model is established for each PF component, and the model parameters and residual variance are extracted as fault eigenvector. It is used as input of neural network classifier to identify rotor working state and fault type. Compared with the intrinsic mode function decomposition (EMD) method, it is shown that the two methods can be effectively applied to the fault diagnosis of rotor systems. But the LMD method has more advantages in signal decomposition. 3. In order to solve the frequency confusion problem of LMD decomposition method, a fault diagnosis method for rotor system based on improved LMD and singular value decomposition (SVD) is proposed. In this method, the rotor vibration signal is decomposed into several wavelet packet components by wavelet packet decomposition method, and a series of PF components are obtained by LMD decomposition of each wavelet packet component, forming the initial eigenvector matrix. Then the singular value of the initial eigenvector matrix is obtained by singular value decomposition. The singular value of the matrix is input into the neural network as the eigenvector to identify the working state and fault type of the rotor system. Experimental results show that this method can be used effectively in rotor system fault diagnosis. 4. A fault diagnosis method for rotor system based on improved LMD and time-frequency entropy is proposed. After the vibration signal of rotor system is decomposed by wavelet LMD, the energy distribution on PF component with different time scale is different. The distribution of energy in different PF components is different and the time-frequency distribution is different. The time-frequency entropy based on the improved LMD method is the quantitative description of the time-frequency distribution. The time-frequency entropy based on improved LMD is very sensitive to rotor fault types and can be used in rotor system fault diagnosis.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3

【引證文獻】

相關(guān)碩士學(xué)位論文 前2條

1 王東方;面向云計算的設(shè)備故障診斷系統(tǒng)關(guān)鍵技術(shù)研究[D];鄭州大學(xué);2012年

2 董曉華;局部均值分解在旋轉(zhuǎn)機械振動中的研究與應(yīng)用[D];燕山大學(xué);2012年

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本文編號：1882514