本文關(guān)鍵詞： 故障診斷 滾動(dòng)軸承 智能診斷 稀疏表達(dá) l_1優(yōu)化 字典學(xué)習(xí) 時(shí)頻分布 過完備 結(jié)構(gòu)稀疏 流形學(xué)習(xí) 圖嵌入 深度學(xué)習(xí) 深度信念網(wǎng)絡(luò)　出處：《中國(guó)科學(xué)技術(shù)大學(xué)》2017年博士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)代科學(xué)技術(shù)的迅猛發(fā)展讓越來越多的先進(jìn)技術(shù)融入于機(jī)械設(shè)備的故障診斷領(lǐng)域,并取得了令人驚嘆的成績(jī),這使當(dāng)今的故障診斷上升到了一個(gè)新的臺(tái)面。稀疏表達(dá)技術(shù)作為新興的信號(hào)處理方法,其自身優(yōu)勢(shì)十分適用于機(jī)械故障診斷。本文從稀疏表達(dá)理論的研究成果出發(fā),探索了該方法對(duì)于機(jī)械故障信號(hào)的特征提取和故障診斷等方面的應(yīng)用潛力。旋轉(zhuǎn)機(jī)械在現(xiàn)代工業(yè)和生產(chǎn)中占有越來越大的比重。對(duì)旋轉(zhuǎn)機(jī)械的運(yùn)轉(zhuǎn)異常做出及時(shí)預(yù)警不僅可以保證其運(yùn)作的安全性,還可以帶來明顯的經(jīng)濟(jì)收益。滾動(dòng)軸承作為實(shí)現(xiàn)其核心功能的關(guān)鍵部件在旋轉(zhuǎn)機(jī)械中有廣泛的應(yīng)用,其運(yùn)行狀態(tài)的可靠性與否關(guān)系到整個(gè)機(jī)械系統(tǒng)的工作性能。因此,對(duì)于新的故障診斷方法的探索和開展,滾動(dòng)軸承是一個(gè)很好的研究對(duì)象�；谏鲜隹紤],本文從滾動(dòng)軸承的故障診斷出發(fā),基于稀疏表達(dá)理論提出了一系列的故障特征提取和故障診斷方法。主要內(nèi)容如下:1.從歷史背景、科技發(fā)展和實(shí)際工程案例等方面詳細(xì)闡述了機(jī)械設(shè)備的狀態(tài)監(jiān)測(cè)和故障診斷這一選題的研究意義。以滾動(dòng)軸承為研究對(duì)象,從其故障機(jī)理和信號(hào)特點(diǎn)出發(fā),回顧并分析了現(xiàn)有的診斷研究方法,包括時(shí)域上的、頻域上的和時(shí)頻域上的診斷方法,并基于國(guó)內(nèi)外的研究成果和研究現(xiàn)狀討論了各個(gè)方法的優(yōu)缺點(diǎn)和當(dāng)今故障診斷研究的不足方面。對(duì)智能診斷技術(shù)做了概述,并著重介紹了目前熱門的神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)技術(shù)。詳細(xì)闡述了稀疏表達(dá)理論在故障診斷領(lǐng)域的三個(gè)主要方面的研究應(yīng)用,即信號(hào)降噪、特征提取和故障分類,并在最后提出了本文研究的主要思路。2.對(duì)稀疏表達(dá)理論的基本概念做了詳細(xì)介紹,從數(shù)學(xué)模型出發(fā)提出了稀疏系數(shù)求解和字典設(shè)計(jì)兩個(gè)稀疏表達(dá)理論中的主要問題,并結(jié)合現(xiàn)有的研究狀況對(duì)這兩類問題分別詳細(xì)闡述了幾個(gè)常用方法且做了簡(jiǎn)單的比較分析。這些理論介紹為后續(xù)章節(jié)提出的診斷方法奠定了理論基礎(chǔ)。3.針對(duì)傳統(tǒng)小波變換的小波函數(shù)振動(dòng)模式與機(jī)械故障信號(hào)的振動(dòng)模式匹配不足這一缺陷,結(jié)合稀疏表達(dá)理論提出了一種可調(diào)小波振動(dòng)模式的過完備小波變換。隨后,以應(yīng)用對(duì)比的方式分析了該變換相比傳統(tǒng)時(shí)頻變換用于特征提取的優(yōu)勢(shì),并從中提取出可用于故障診斷的SWE特征。實(shí)驗(yàn)驗(yàn)證SWE特征相較以傳統(tǒng)時(shí)頻方法得到特征的分類性能更出色。4.利用結(jié)構(gòu)稀疏表達(dá)理論建立起表征數(shù)據(jù)關(guān)系的結(jié)構(gòu)字典并對(duì)各類信號(hào)進(jìn)行有效表征,使得同類型的具有相似模式的滾動(dòng)軸承振動(dòng)信號(hào)具有統(tǒng)一的表達(dá)模式。系數(shù)求解過程中基于混合約束項(xiàng)進(jìn)行計(jì)算優(yōu)化,使得最終表達(dá)在組層面上和結(jié)構(gòu)層面上均進(jìn)行特征選擇,準(zhǔn)確把握信號(hào)的類別和結(jié)構(gòu)模式。為了便于后續(xù)的分析診斷,從表達(dá)中進(jìn)一步提出了低維故障特征SSW,并以實(shí)驗(yàn)證明該特征具有很強(qiáng)的噪音抑制能力和穩(wěn)定性,可有效實(shí)現(xiàn)軸承的故障診斷。5.將數(shù)據(jù)的流形學(xué)習(xí)理論與稀疏表達(dá)理論相結(jié)合,針對(duì)滾動(dòng)軸承故障診斷問題的解決提出了 ManiSC特征提取框架。該方法首先利用數(shù)據(jù)的先驗(yàn)知識(shí)建立起表征數(shù)據(jù)關(guān)系的圖譜矩陣,再以流形學(xué)習(xí)方式找到數(shù)據(jù)的基矩陣,并將數(shù)據(jù)映射至稀疏域。實(shí)驗(yàn)證明了 ManiSC特征能以低維的方式有效表征出原始高維數(shù)據(jù)中的幾何特性和內(nèi)在數(shù)據(jù)結(jié)構(gòu),并比標(biāo)準(zhǔn)的稀疏表達(dá)和流形學(xué)習(xí)方法具有更出色的魯棒性和特征辨識(shí)度。6.將稀疏表達(dá)與深度學(xué)習(xí)算法中的深度信念網(wǎng)絡(luò)相結(jié)合(稀疏DBN),用其建立了分模塊故障診斷網(wǎng)絡(luò),使一次診斷可以在評(píng)估出軸承故障位置的同時(shí)判斷出故障嚴(yán)重程度。滾動(dòng)軸承的壽命離散性特點(diǎn)會(huì)導(dǎo)致使用中的軸承存在先后失效狀況,若不及時(shí)診斷則存在巨大的安全隱患。本文建立的分模塊故障診斷網(wǎng)絡(luò)能有效解決這個(gè)問題,且實(shí)驗(yàn)的對(duì)比分析說明利用稀疏DBN所搭建的診斷網(wǎng)絡(luò)對(duì)于軸承的狀態(tài)判斷準(zhǔn)確度極高,在工程上具有強(qiáng)大的應(yīng)用潛力。
[Abstract]:The field of fault diagnosis of the development of modern science and technology makes more and more advanced technology in machinery and equipment, and achieved amazing results, the fault diagnosis of today's rise to a new table. The sparse expression as a signal processing method of emerging technology, its advantage is very suitable for mechanical fault diagnosis. This paper from the research results of the theory of sparse representation, explores the method for the potential applications of fault signal feature extraction and fault diagnosis of rotating machinery. Play more and more important role in modern industry and production. The abnormal operation of rotating machinery make a timely warning can not only ensure the safety of its operation, but also it can bring obvious economic benefits. The rolling bearing is a key component to realize the core function is widely used in rotating machinery, its running status can be Reliability relates to the performance of the entire mechanical system. Therefore, to explore new methods for fault diagnosis of rolling bearing and the development, is a good research object. Based on the above considerations, this paper from the rolling bearing fault diagnosis based on sparse representation theory, fault feature extraction and fault diagnosis method of a series of the main contents are as follows: 1.. Based on the historical background, science and technology development and practical engineering cases described in detail the condition monitoring and fault diagnosis of mechanical equipment on the significance of this research. Taking the rolling bearing as the research object, starting from the fault mechanism and signal characteristics, review and analysis of the diagnosis of existing methods. Including time domain, frequency domain of the diagnostic method in the frequency domain and time, and research results and research status at home and abroad are discussed based on the advantages and disadvantages of each method and the fault diagnosis. The lack of intelligent diagnosis technology are summarized, and emphatically introduces the current popular neural network and support vector machine technology. Elaborated the sparse representation theory in the field of fault diagnosis of the three main aspects of the research, namely signal de-noising, feature extraction and fault classification, and finally puts forward the main.2. thought this study on the sparse representation of the basic concepts of the theory in detail, from the mathematical model of the proposed sparse coefficients and two sparse dictionary design expression of major problems in the theory, combined with the existing research condition of the two kinds of problems are expounded, several commonly used methods and made simple comparative analysis. Laid the theoretical basis for the traditional vibration.3. wavelet transform and wavelet function vibration modes and fault signals of these theories introduced as diagnostic methods proposed in subsequent chapters Pattern matching overcomes the defect based on sparse theory proposed a wavelet adjustable vibration mode of overcomplete wavelet transform expression. Then, by way of contrast analysis of the transform compared to the traditional time-frequency transform for feature extraction, and extracted SWE can be used for fault diagnosis. The feature classification performance experiment verify the SWE characteristics and frequency characteristics of the traditional method are compared with the more excellent.4. structure using sparse representation theory to establish the relationship between the structure and characterization of data dictionary for effective representation of all kinds of signals, making the same type with a similar mode of rolling bearing vibration signal model with the unified expression. Based on the mixed constraint coefficient in the process of solving calculation optimization, making the final expression characteristics were carried out in the group level and structure level, accurately grasp the categories and structure of the model in order to facilitate the signal. Diagnostic analysis of follow-up, from the expression of the proposed low dimensional fault feature of SSW, and the experimental results show that the characteristics of strong noise suppression ability and stability, can effectively realize the bearing fault diagnosis.5. the data manifold learning theory and sparse representation theory are combined to solve the problem of fault diagnosis of rolling bearing is put forward ManiSC feature extraction framework. This method uses the data matrix of a priori knowledge of the established data representation of relations, and then to the manifold learning way to find the basis matrix data, and the data is mapped to the sparse domain. The experimental results show the characteristics of ManiSC with low dimensional effective characterization of geometric characteristics of the original high dimensional data and the internal data structure, and than the sparse expression and standard manifold learning method has better robustness and distinguishing features.6. learning algorithm and the depth of the sparse representation With the deep belief network (DBN, with its sparse) sub module fault diagnosis network is established, so that a diagnosis of bearing fault location and fault severity evaluation in life. The discrete characteristics of rolling bearings will lead to bearing used in the existing failure status after the first, if not timely diagnosis there is a huge security risk. This paper established the sub module fault diagnosis network can effectively solve this problem, and the comparative analysis of the diagnostic network using sparse DBN built for judging the state of bearing with high accuracy, has a strong application potential in engineering.

【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH133.33

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