【摘要】：制造業(yè)是科技革新的動脈,高端設(shè)備制造業(yè)更是關(guān)乎一個國家社會安全、國防安全和經(jīng)濟安全的新興戰(zhàn)略產(chǎn)業(yè)。隨著以滾動軸承為代表的旋轉(zhuǎn)機械零部件的廣泛應(yīng)用,制造業(yè)發(fā)展革新也面臨著設(shè)備維護、機械檢修、壽命預(yù)測、狀態(tài)識別等一系列問題。針對不同原因、不同表征、不同程度的機械故障的智能化檢測技術(shù)已成為當(dāng)前的研究熱點和難點。本課題以滾動軸承為研究對象,基于故障機理知識,開展信號采集、信號分析、信號處理等技術(shù)的研究,實現(xiàn)智能化檢測,具體內(nèi)容如下:(1)闡述滾動軸承的結(jié)構(gòu)特性、分類形式和失效種類。從故障機理的角度,對滾動軸承進行運動學(xué)分析,分別對滾動軸承動力學(xué)、故障荷載、故障信號進行建模。與此同時,研究討論故障出現(xiàn)的位置對振動信號特征的影響。(2)基于信號處理和機器學(xué)習(xí)方法的先驗知識,分別提出基于自回歸和譜熵算法的檢測技術(shù)和基于字典和增強學(xué)習(xí)算法的檢測技術(shù)。前者采用自回歸模型剔除機械振動信號中可線性預(yù)測的平穩(wěn)成分,在不同頻帶下進行復(fù)Morlet小波包絡(luò),結(jié)合譜熵在頻域內(nèi)與通帶濾波的相關(guān)性選定最優(yōu)包絡(luò)。后者采用字典學(xué)習(xí)和稀疏編碼對機械振動信號進行預(yù)處理,并引入反向傳播神經(jīng)網(wǎng)絡(luò)構(gòu)建自適應(yīng)增強學(xué)習(xí)的分類函數(shù),采用機器學(xué)習(xí)方法訓(xùn)練集實現(xiàn)智能化檢測�；诜抡婧驮囼灲Y(jié)果驗證提出技術(shù)的有效性和先進性,在工程應(yīng)用中具有良好的前景。(3)從試驗臺機械結(jié)構(gòu)設(shè)計、控制系統(tǒng)運行通信以及軟件開發(fā)三個方面搭建檢測平臺。參與電機和滾動軸承的選型,加速度傳感器的安裝及信號采集,控制系統(tǒng)的編程,數(shù)據(jù)可視化,數(shù)值計算分析,人機交互界面設(shè)計等工作。將故障機理和提出技術(shù)應(yīng)用于滾動軸承故障檢測平臺,試驗效果顯著。
[Abstract]:Manufacturing industry is the artery of scientific and technological innovation. High-end equipment manufacturing industry is related to a country's social security, national defense security and economic security emerging strategic industries. With the extensive application of rotating machinery parts represented by rolling bearings, the development and innovation of manufacturing industry is faced with a series of problems, such as equipment maintenance, mechanical maintenance, life prediction, state recognition and so on. According to different reasons, different representations, different degrees of intelligent detection technology of mechanical faults has become a hot and difficult research. This subject takes rolling bearing as the research object, based on the knowledge of fault mechanism, carries out the research of signal acquisition, signal analysis, signal processing and so on, and realizes intelligent detection. The specific contents are as follows: (1) expound the structural characteristics of rolling bearing. Classification form and failure type. From the point of view of fault mechanism, the kinematics analysis of rolling bearing is carried out, and the dynamics, fault load and fault signal of rolling bearing are modeled respectively. At the same time, the influence of fault location on vibration signal characteristics is discussed. (2) A priori knowledge based on signal processing and machine learning, The detection techniques based on autoregressive and spectral entropy algorithms and dictionary and enhanced learning algorithms are proposed respectively. The former uses autoregressive model to eliminate the linear predictable stationary components of mechanical vibration signals and carries out complex Morlet wavelet envelopes in different frequency bands. The optimal envelope is selected in combination with the correlation between spectral entropy and passband filtering in frequency domain. The latter uses dictionary learning and sparse coding to preprocess mechanical vibration signal, and introduces back propagation neural network to construct classification function of adaptive reinforcement learning. Machine learning method training set is used to realize intelligent detection. Based on the simulation and experimental results, the effectiveness and advanced nature of the proposed technology are verified, and it has a good prospect in engineering application. (3) the testing platform is built from three aspects: the mechanical structure design of the test-bed, the running communication of the control system and the software development. It is involved in the selection of motor and rolling bearing, the installation of acceleration sensor and signal collection, the programming of control system, data visualization, numerical calculation and analysis, and the design of man-machine interface. The fault mechanism and the proposed technology are applied to the rolling bearing fault detection platform, and the test results are remarkable.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH133.33

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