本文關(guān)鍵詞：基于HHT與神經(jīng)網(wǎng)絡(luò)的旋轉(zhuǎn)機(jī)械故障診斷研究　出處：《南京航空航天大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 故障診斷 Hilbert-Huang變換 神經(jīng)網(wǎng)絡(luò) 遺傳算法 特征提取 轉(zhuǎn)子系統(tǒng)

【摘要】：旋轉(zhuǎn)機(jī)械是航空、電力、化工等眾多領(lǐng)域的關(guān)鍵設(shè)備，所以對(duì)其進(jìn)行故障診斷研究具有重要的現(xiàn)實(shí)意義。而隨著振動(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)、處理和分析為基礎(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)神經(jīng)網(wǎng)絡(luò)、遺傳算法等理論的發(fā)展也為故障診斷技術(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Hilbert-Huang變換（簡(jiǎn)稱HHT）方法以及神經(jīng)網(wǎng)絡(luò)等相關(guān)內(nèi)容。一方面，介紹了HHT方法的基本原理和實(shí)現(xiàn)過程，并分析了該方法存在的端點(diǎn)效應(yīng)和虛假模態(tài)問題；另一方面，介紹了BP神經(jīng)網(wǎng)絡(luò)和遺傳算法的基本理論，并研究了遺傳算法優(yōu)化BP網(wǎng)絡(luò)的過程，即針對(duì)BP網(wǎng)絡(luò)的不足采用遺傳算法進(jìn)行優(yōu)化。 同時(shí)，為研究旋轉(zhuǎn)機(jī)械的故障診斷問題，采用多功能轉(zhuǎn)子試驗(yàn)臺(tái)模擬旋轉(zhuǎn)機(jī)械的常見故障，并運(yùn)用HHT方法對(duì)各故障信號(hào)進(jìn)行處理和分析，在此基礎(chǔ)上，利用模糊熵能夠表示信號(hào)復(fù)雜程度且具有相對(duì)穩(wěn)定性等特點(diǎn)，將模糊熵理論引入到故障診斷領(lǐng)域，并提出了一種基于EMD和模糊熵相結(jié)合的特征向量提取方法，同時(shí)將它用于轉(zhuǎn)子故障的特征提取中，證明了該特征提取方法的可行性和有效性。 最后，綜合地運(yùn)用HHT方法和經(jīng)遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)進(jìn)行轉(zhuǎn)子系統(tǒng)的故障診斷研究。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經(jīng)遺傳算法優(yōu)化的BP網(wǎng)絡(luò)模型中進(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 many fields, such as aviation, electric power, chemical industry and so on. Therefore, it is of great practical significance to study the fault diagnosis of rotating machinery. However, with the development of vibration detection and signal processing and other related technologies. Fault diagnosis technology based on vibration signal detection, processing and analysis has become an important research direction in the field of fault diagnosis, and neural network. The development of genetic algorithm also opens a new way for the research and application of fault diagnosis technology. In this paper, the Hilbert-Huang transform method and neural network are introduced in detail. On the one hand, the basic principle and implementation process of HHT method are introduced. The endpoint effect and the false modal problem of the method are analyzed. On the other hand, the basic theory of BP neural network and genetic algorithm is introduced, and the process of optimizing BP network by genetic algorithm is studied. At the same time, in order to study the fault diagnosis of rotating machinery, the common faults of rotating machinery are simulated by multi-function rotor test-bed, and each fault signal is processed and analyzed by using HHT method. The fuzzy entropy theory is introduced into the field of fault diagnosis by using the characteristics of fuzzy entropy which can express the signal complexity and has relative stability. A feature vector extraction method based on the combination of EMD and fuzzy entropy is proposed and applied to the feature extraction of rotor faults. The feasibility and effectiveness of the feature extraction method are proved. Finally, the fault diagnosis of rotor system is studied by using HHT method and BP neural network optimized by genetic algorithm, and the characteristic quantity of common faults of rotor system is extracted. Then the feature is input into the BP neural network model optimized by genetic algorithm 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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