發(fā)布時間：2018-05-03 00:26

  本文選題：故障診斷 + 軸向柱塞泵��； 參考：《燕山大學(xué)》2012年碩士論文

【摘要】：隨著液壓技術(shù)不斷地發(fā)展，液壓系統(tǒng)被廣泛地應(yīng)用于許多重要的領(lǐng)域。在液壓系統(tǒng)的功能不斷增強的同時，其結(jié)構(gòu)變得越來越復(fù)雜，這也增大了液壓系統(tǒng)發(fā)生故障的可能性。液壓泵作為整個液壓系統(tǒng)的動力源，它所處的工作環(huán)境惡劣，并且結(jié)構(gòu)復(fù)雜，導(dǎo)致其很容易發(fā)生故障。它的工作狀況將成為影響整個液壓系統(tǒng)乃至整個設(shè)備的正常工作的關(guān)鍵，因而對液壓泵進(jìn)行狀態(tài)監(jiān)測和故障診斷尤為重要。近年來，液壓泵的故障診斷技術(shù)成為研究的熱點，正向著智能化、自動化的方向發(fā)展。本文采用了一種將混沌理論和神經(jīng)網(wǎng)絡(luò)相結(jié)合的方法完成液壓泵的故障診斷過程。 混沌理論是當(dāng)今非線性科學(xué)研究非�；钴S的一個方面，將混沌理論和神經(jīng)網(wǎng)絡(luò)相結(jié)合構(gòu)成性能更為優(yōu)越的混沌神經(jīng)網(wǎng)絡(luò)成為研究的熱點之一。本文在前向神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)之上，，建立了一種基于Logistic映射的前向混沌神經(jīng)網(wǎng)絡(luò)，并研究了該網(wǎng)絡(luò)的學(xué)習(xí)算法。通過引入混沌機制，使得該種混沌神經(jīng)網(wǎng)絡(luò)能夠有效地避免神經(jīng)網(wǎng)絡(luò)在訓(xùn)練過程中易陷入局部極小值的缺點，并對微小區(qū)別的模式具有更好的識別效果，該網(wǎng)絡(luò)具有良好的尋優(yōu)能力、泛化能力以及模式識別能力。 為了驗證該方法的有效性，本文以實驗室材料試驗機的斜盤式軸向柱塞泵為診斷對象，對液壓泵進(jìn)行狀態(tài)監(jiān)測，采集了泵在不同工作狀態(tài)下在其端蓋處的振動信號。以垂直于端蓋的振動信號為研究信息，采用短時最大熵譜分析的方法得出了各故障狀態(tài)的共振頻帶范圍，為小波包帶通濾波提供依據(jù)。利用小波包理論和希爾伯特變換的包絡(luò)解調(diào)的方法完成信號的處理，并進(jìn)行了功率譜分析。提取包絡(luò)信號的幅值域特征指標(biāo)作為特征向量，以多組特征向量作為混沌神經(jīng)網(wǎng)絡(luò)的訓(xùn)練和測試輸入。應(yīng)用MATLAB軟件進(jìn)行編程，證明了前向混沌神經(jīng)網(wǎng)絡(luò)應(yīng)用在液壓泵故障診斷中是切實可行的，并且與目前應(yīng)用廣泛的BP神經(jīng)網(wǎng)絡(luò)的診斷結(jié)果相比較，得出前向混沌神經(jīng)網(wǎng)絡(luò)比L-M優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)的收斂速度更快、診斷正確率更高，體現(xiàn)了混沌神經(jīng)網(wǎng)絡(luò)應(yīng)用于液壓泵故障診斷方面的優(yōu)越性。
[Abstract]:With the development of hydraulic technology, hydraulic system is widely used in many important fields. At the same time, the structure of hydraulic system becomes more and more complex, which increases the possibility of hydraulic system failure. As the power source of the whole hydraulic system, the hydraulic pump is in a bad working environment and complex structure, which leads to its failure easily. Its working condition will be the key to affect the whole hydraulic system and even the whole equipment, so the condition monitoring and fault diagnosis of hydraulic pump is very important. In recent years, the fault diagnosis technology of hydraulic pump has become a hot spot, and is developing towards the direction of intelligence and automation. In this paper, a method combining chaos theory and neural network is used to complete the fault diagnosis of hydraulic pump. Chaotic theory is one of the most active aspects of nonlinear science nowadays. The combination of chaos theory and neural network to form a chaotic neural network with better performance has become one of the research hotspots. In this paper, based on the forward neural network, a forward chaotic neural network based on Logistic mapping is established, and the learning algorithm of the network is studied. By introducing chaos mechanism, this kind of chaotic neural network can effectively avoid the shortcoming that the neural network is easy to fall into the local minimum value in the training process, and has better recognition effect to the pattern of small difference. The network has good optimization ability, generalization ability and pattern recognition ability. In order to verify the effectiveness of this method, this paper takes the oblique disc axial piston pump of the laboratory material testing machine as the diagnostic object, carries on the condition monitoring to the hydraulic pump, and collects the vibration signal of the pump at the end cover of the pump in different working state. Taking the vibration signal perpendicular to the end cover as the research information, the resonance frequency band range of each fault state is obtained by using the method of short-time maximum entropy spectrum analysis, which provides the basis for the wavelet packet bandpass filtering. The wavelet packet theory and the envelope demodulation method of Hilbert transform are used to complete the signal processing, and the power spectrum analysis is carried out. The amplitude range feature index of the envelope signal is extracted as the feature vector, and the multi-group eigenvector is used as the training and test input of the chaotic neural network. The application of forward chaotic neural network in hydraulic pump fault diagnosis is proved to be feasible by using MATLAB software, and compared with the result of BP neural network, which is widely used at present. It is concluded that the forward chaotic neural network has faster convergence speed and higher diagnostic accuracy than the L-M optimized BP neural network, which shows the superiority of chaotic neural network in hydraulic pump fault diagnosis.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3;TP183

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本文編號：1836139