發(fā)布時(shí)間：2018-07-10 14:39

  本文選題：電機(jī) + 故障診斷��； 參考：《蘭州理工大學(xué)》2014年碩士論文

【摘要】：電機(jī)是最主要的機(jī)電能量轉(zhuǎn)換設(shè)備,不論是在國(guó)民經(jīng)濟(jì)中的各種能源、制造領(lǐng)域里,還是在人們的日常生活中,電機(jī)都有著無(wú)可替代的地位。電機(jī)故障診斷技術(shù)的研究具有重大的經(jīng)濟(jì)意義和社會(huì)意義,經(jīng)歷了幾十年的發(fā)展,電機(jī)故障診斷技術(shù)取得了長(zhǎng)足進(jìn)步,不論是在信號(hào)處理方面還是在診斷方法上與發(fā)展之初相比都己不可同日而語(yǔ),然而,目前普遍采用的基于單參數(shù)、單特征的電機(jī)故障診斷系統(tǒng)在診斷過(guò)程中仍存在很大的不確定性,有時(shí)往往難以保證診斷的精確度,在此基礎(chǔ)上,本文構(gòu)建了一種基于多傳感器信息融合技術(shù)的電機(jī)故障診斷方法。 本文以電機(jī)故障診斷為研究對(duì)象,首先介紹了電機(jī)故障診斷技術(shù)的背景、意義及發(fā)展,分析了電機(jī)故障診斷技術(shù)的發(fā)展趨勢(shì),同時(shí)對(duì)多傳感器信息融合技術(shù)進(jìn)行了簡(jiǎn)介,并對(duì)電機(jī)定子故障、轉(zhuǎn)子故障、軸承故障及氣隙偏心故障等常見(jiàn)故障進(jìn)行了分析。 在信號(hào)處理及特征提取方面,針對(duì)希爾伯特-黃變換的核心內(nèi)容經(jīng)驗(yàn)?zāi)B(tài)分解中存在的模態(tài)混疊以及虛假分量問(wèn)題進(jìn)行了改進(jìn)。通過(guò)仿真實(shí)驗(yàn),驗(yàn)證了集合經(jīng)驗(yàn)?zāi)B(tài)分解在抑制模態(tài)混疊現(xiàn)象時(shí)的可行性；采用了利用灰色關(guān)聯(lián)度進(jìn)行虛假分量識(shí)別的方法,通過(guò)與相關(guān)系數(shù)法的對(duì)比仿真,證明了灰色關(guān)聯(lián)度在識(shí)別虛假分量時(shí)的有效性。并在此基礎(chǔ)上,利用各固有模態(tài)函數(shù)能量構(gòu)造故障特征向量。在故障局部診斷方法上,采用了目前應(yīng)用最為廣泛、理論最為成熟的BP神經(jīng)網(wǎng)絡(luò),介紹了神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)知識(shí)、原理、結(jié)構(gòu)及學(xué)習(xí)過(guò)程,利用神經(jīng)網(wǎng)絡(luò)的優(yōu)良特性為后續(xù)的基于D-S證據(jù)理論的信息融合方法提供精度和可靠性更高的輸入信息。在信息融合算法方面,對(duì)D-S證據(jù)理論的基本概念及D-S合成規(guī)則進(jìn)行了介紹和分析,在基本可信度分配函數(shù)的構(gòu)成上,利用神經(jīng)網(wǎng)絡(luò)局部診斷結(jié)果作為基礎(chǔ)綜合考慮誤差因素,不僅解決了D-S證據(jù)理論中如何構(gòu)建基本可信度分配函數(shù)的難點(diǎn),同時(shí)也避免了D-S合成規(guī)則難以處理沖突證據(jù)的缺陷,將神經(jīng)網(wǎng)絡(luò)與D-S證據(jù)理論有效結(jié)合起來(lái)。 最后,本文構(gòu)建了基于多傳感器信息融合技術(shù)的電機(jī)故障診斷系統(tǒng)模型,并選擇電機(jī)故障中最為常見(jiàn)的軸承故障作為實(shí)驗(yàn)對(duì)象,對(duì)診斷系統(tǒng)進(jìn)行了實(shí)驗(yàn)驗(yàn)證和數(shù)據(jù)分析。通過(guò)試驗(yàn),驗(yàn)證了本文所構(gòu)建的基于多傳感器信息融合的電機(jī)故障診斷系統(tǒng)具有可行性、正確性和有效性。
[Abstract]:Motor is the most important electromechanical energy conversion equipment, whether in the national economy in all kinds of energy, manufacturing field, or in the daily life of people, the motor has an irreplaceable position. The research of motor fault diagnosis technology has great economic and social significance. After decades of development, the motor fault diagnosis technology has made great progress. Neither in signal processing nor in diagnostic methods have been compared with the beginning of its development. However, it is widely used at present on the basis of single parameter. The single feature motor fault diagnosis system still has a lot of uncertainty in the process of diagnosis, and sometimes it is difficult to guarantee the accuracy of the diagnosis. In this paper, a method of motor fault diagnosis based on multi-sensor information fusion technology is proposed. In this paper, motor fault diagnosis is taken as the research object. Firstly, the background, significance and development of motor fault diagnosis technology are introduced, and the development trend of motor fault diagnosis technology is analyzed. At the same time, the multi-sensor information fusion technology is introduced briefly. The common faults such as stator fault, rotor fault, bearing fault and air gap eccentricity fault are analyzed. In the aspect of signal processing and feature extraction, the problems of modal aliasing and false components in empirical mode decomposition of Hilbert-Huang transform are improved. Through the simulation experiment, the feasibility of the set empirical mode decomposition in suppressing the mode aliasing phenomenon is verified, and the method of using grey correlation degree to identify the false component is adopted, and the simulation results are compared with the correlation coefficient method. The validity of grey correlation degree in identifying false components is proved. On this basis, the fault eigenvector is constructed by using the energy of each inherent mode function. In the method of fault local diagnosis, BP neural network, which is the most widely used and the most mature theory at present, is adopted. The basic knowledge, principle, structure and learning process of neural network are introduced. The advantages of neural network can provide more accurate and reliable input information for the subsequent information fusion method based on D-S evidence theory. In the aspect of information fusion algorithm, the basic concept of D-S evidence theory and D-S synthesis rule are introduced and analyzed. In the structure of basic reliability distribution function, the error factors are synthetically considered based on the local diagnosis result of neural network. It not only solves the difficulty of how to construct the basic reliability assignment function in D-S evidence theory, but also avoids the defect that D-S synthesis rule is difficult to deal with conflict evidence, and combines neural network and D-S evidence theory effectively. Finally, a fault diagnosis system model of motor based on multi-sensor information fusion technology is constructed, and the most common bearing fault in motor fault is selected as the experimental object, and the experimental verification and data analysis of the diagnosis system are carried out. The experimental results show that the motor fault diagnosis system based on multi-sensor information fusion is feasible, correct and effective.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM307

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