發(fā)布時(shí)間：2018-08-31 13:47

【摘要】：旋轉(zhuǎn)機(jī)械是航空、化工、電力等領(lǐng)域的關(guān)鍵設(shè)備，因此對(duì)其進(jìn)行故障診斷研究具有重大的現(xiàn)實(shí)意義。隨著振動(dòng)檢測(cè)和信號(hào)處理等相關(guān)技術(shù)的不斷發(fā)展，以振動(dòng)信號(hào)檢測(cè)、處理與分析為基礎(chǔ)的故障診斷技術(shù)已成為故障診斷領(lǐng)域一個(gè)重要的研究方向。同時(shí)基于神經(jīng)網(wǎng)絡(luò)的智能故障識(shí)別與診斷技術(shù)的研究，也為故障診斷技術(shù)的研究和應(yīng)用開辟了一條嶄新的途徑。 本文詳細(xì)介紹了小波變換以及BP神經(jīng)網(wǎng)絡(luò)等相關(guān)內(nèi)容。一方面，介紹了連續(xù)小波變換、離散小波變換、正交小波包變換等內(nèi)容，并且分析了邊沿效應(yīng)的問題；另一方面，介紹了BP神經(jīng)網(wǎng)絡(luò)的基本原理，分析了標(biāo)準(zhǔn)BP神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)算法和存在的問題，研究了一種基于BP算法的小波神經(jīng)網(wǎng)絡(luò)，并通過仿真實(shí)例對(duì)BP神經(jīng)網(wǎng)絡(luò)與基于BP算法的小波神經(jīng)網(wǎng)絡(luò)性能進(jìn)行了對(duì)比。 同時(shí)，為了提取旋轉(zhuǎn)機(jī)械故障特征，研究了基于連續(xù)小波變換極大模值法和基于最優(yōu)正交小波包法提取故障特征值的理論及具體實(shí)現(xiàn)過程。 最后，，通過多功能轉(zhuǎn)子試驗(yàn)臺(tái)模擬旋轉(zhuǎn)機(jī)械的常見故障，運(yùn)用連續(xù)小波變換的模極大值法及正交小波包變換提取轉(zhuǎn)子系統(tǒng)常見故障的特征量，再將該特征量輸入到BP神經(jīng)網(wǎng)絡(luò)中進(jìn)行故障診斷，結(jié)果表明上述方法應(yīng)用在轉(zhuǎn)子系統(tǒng)故障診斷中能夠取得較好的效果。
[Abstract]:Rotating machinery is the key equipment in the fields of aviation, chemical industry and electric power, so it is of great practical significance to study the fault diagnosis of rotating machinery. With the development of vibration detection and signal processing, fault diagnosis based on vibration signal detection, processing and analysis has become an important research direction in the field of fault diagnosis. At the same time, the research of intelligent fault identification and diagnosis technology based on neural network also opens a new way for the research and application of fault diagnosis technology. In this paper, wavelet transform and BP neural network are introduced in detail. On the one hand, the continuous wavelet transform, discrete wavelet transform and orthogonal wavelet packet transform are introduced, and the problem of edge effect is analyzed. On the other hand, the basic principle of BP neural network is introduced. This paper analyzes the learning algorithm and existing problems of standard BP neural network, studies a wavelet neural network based on BP algorithm, and compares the performance of BP neural network with wavelet neural network based on BP algorithm through simulation examples. At the same time, in order to extract the fault features of rotating machinery, the theory and implementation process of extracting fault eigenvalues based on the maximum modulus method of continuous wavelet transform and the optimal orthogonal wavelet packet method are studied. Finally, the common faults of rotating machinery are simulated on a multifunctional rotor test-bed, and the characteristic quantities of common faults of rotor system are extracted by modulus maximum method of continuous wavelet transform and orthogonal wavelet packet transform. Then the eigenvalue is input into the BP neural network for fault diagnosis. The results show that the above method can achieve good results in rotor system fault diagnosis.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

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