【摘要】：目前，振動檢測是大型旋轉(zhuǎn)機械故障診斷的主要手段，一般旋轉(zhuǎn)機械發(fā)生故障時會產(chǎn)生復雜的動態(tài)非平穩(wěn)振動信號，故如何準確地提取出此類信號的特征量是故障診斷的首要條件。諧波小波理論基于其頻域嚴格的“盒形”特性，非常適合于非平穩(wěn)信號的特征提取，但是由于旋轉(zhuǎn)機械振動信號本身的復雜性，該方法還沒有在旋轉(zhuǎn)機械的故障診斷領域得到廣泛的應用。本文在其他學者研究的基礎上，提出了諧波小波包對旋轉(zhuǎn)機械振動信號能量特征自動提取的方法，避免了轉(zhuǎn)速和采樣頻率不同對信號特征提取的影響。 本文首先對旋轉(zhuǎn)機械故障診斷意義及其發(fā)展狀況做了簡單的介紹，選擇了幾種比較典型的傳統(tǒng)信號處理方法，對它們的優(yōu)缺點進行了對比研究；然后對諧波小波理論做了系統(tǒng)的研究，通過仿真信號詳細分析了它在微弱信號、局部突變信號和近頻信號的特征提取中的優(yōu)勢；研究了諧波小波包對不同轉(zhuǎn)速和不同采樣頻率下的信號的能量特征提取方法；其次介紹了Elman神經(jīng)網(wǎng)絡的基本結構及其算法，并且與BP網(wǎng)絡進行了對比，突出其在學習穩(wěn)定性、收斂速度和故障識別率方面的優(yōu)勢；最后提出了諧波小波包和Elman神經(jīng)網(wǎng)絡相結合的思想，，設計了基于這種思想的旋轉(zhuǎn)機械故障智能診斷系統(tǒng)的基本結構。 采用LabVIEW和MATLAB相結合的方法，完整地設計了基于諧波小波包和Elman神經(jīng)網(wǎng)絡的旋轉(zhuǎn)機械故障智能診斷系統(tǒng)；通過在轉(zhuǎn)子試驗臺上模擬轉(zhuǎn)子的四種典型故障，采集振動信號，輸入診斷系統(tǒng)，結果顯示該系統(tǒng)診斷性能良好。
[Abstract]:At present, vibration detection is the main means of fault diagnosis for large-scale rotating machinery. In general, complex dynamic and non-stationary vibration signals can be generated when rotating machinery fails. Therefore, how to accurately extract the characteristics of such signals is the first condition of fault diagnosis. Harmonic wavelet theory is very suitable for feature extraction of non-stationary signals based on its strict "box-shaped" characteristics in frequency domain, but because of the complexity of the vibration signal of rotating machinery, This method has not been widely used in fault diagnosis of rotating machinery. In this paper, based on the research of other scholars, a method of automatically extracting the energy feature of rotating machinery vibration signal by harmonic wavelet packet is proposed, which avoids the influence of different rotational speed and sampling frequency on the signal feature extraction. In this paper, the significance and development of rotating machinery fault diagnosis are introduced briefly, and several typical traditional signal processing methods are selected, and their advantages and disadvantages are compared. Then, the harmonic wavelet theory is studied systematically, and its advantages in the feature extraction of weak signal, local mutation signal and near-frequency signal are analyzed in detail through the simulation signal. The energy feature extraction method of harmonic wavelet packet at different rotation speed and different sampling frequency is studied. Secondly, the basic structure and algorithm of Elman neural network are introduced, and compared with BP neural network, its advantages in learning stability, convergence speed and fault recognition rate are highlighted. Finally, the idea of combining harmonic wavelet packet with Elman neural network is put forward, and the basic structure of intelligent fault diagnosis system for rotating machinery is designed based on this idea. The intelligent fault diagnosis system of rotating machinery based on harmonic wavelet packet and Elman neural network is designed based on the method of LabVIEW and MATLAB. By simulating four typical faults of rotor on the rotor test-bed, the vibration signal is collected and the diagnosis system is inputted. The results show that the diagnosis performance of the system is good.
【學位授予單位】：燕山大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3

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