基于流形學(xué)習(xí)的滾動軸承早期故障識別方法研究
發(fā)布時(shí)間：2019-02-14 14:16
【摘要】：滾動軸承作為旋轉(zhuǎn)機(jī)械裝備中關(guān)鍵且易發(fā)生故障的零部件之一,其運(yùn)行狀態(tài)直接影響整個(gè)裝備系統(tǒng)的性能,因此對滾動軸承進(jìn)行狀態(tài)識別與故障診斷研究具有重要意義。信號降噪和故障特征提取是狀態(tài)識別與故障診斷中最核心的內(nèi)容,由于滾動軸承運(yùn)行環(huán)境復(fù)雜,干擾多、噪聲大,且故障信號多為非平穩(wěn)非線性信號,從而大大降低了傳統(tǒng)降噪與故障特征提取方法的有效性。為此,本文以滾動軸承為研究對象,針對其強(qiáng)背景噪聲下的早期故障振動信號特點(diǎn),將流形方法與其它現(xiàn)代振動信號分析方法相結(jié)合,對滾動軸承故障信號的降噪和特征提取問題展開深入研究。論文主要內(nèi)容如下： 1.論述了選題的背景意義及國內(nèi)外滾動軸承故障診斷的發(fā)展進(jìn)程；對當(dāng)代滾動軸承故障診斷中的降噪和故障特征提取兩個(gè)關(guān)鍵問題進(jìn)行了詳細(xì)綜述；剖析了滾動軸承常見故障形式及其成因與后果；闡述了滾動軸承的振動機(jī)理及時(shí)域、頻域、時(shí)頻域振動分析理論的具體使用方法。 2.針對滾動軸承早期微弱故障特征易被噪聲及干擾成分所淹沒的問題,將KPCA流形、EMD和LTSA流形相結(jié)合,提出了改進(jìn)EMD的早期微弱故障信號降噪方法。首先在EMD分解前先進(jìn)行一次KPCA流形降噪,然后在EMD分解基礎(chǔ)上將所有IMF系數(shù)利用LTSA流形算法提取其低維流形分量,并將其求和得到新信號,實(shí)現(xiàn)降噪。該方法不僅充分利用了EMD完全自適應(yīng)性分析非平穩(wěn)非線性信號的優(yōu)勢,還能有效克服噪聲對EMD分解效果的影響,并很好地解決了常規(guī)EMD應(yīng)用中忽略大部分分量造成故障信息丟失的問題。工程實(shí)際信號對比分析驗(yàn)證了該方法的有效性與優(yōu)越性。 3.針對如何有效提取非平穩(wěn)非線性故障信號敏感特征問題,提出了基于二維流形-Hilbert時(shí)頻譜的滾動軸承時(shí)頻故障特征提取方法。首先在Hilbert時(shí)頻分析基礎(chǔ)上,應(yīng)用改進(jìn)LPP算法的二維LPP流形算法提取信號流形成分,然后定義了奇異值熵定量衡量不同故障狀態(tài)下流形特征的差異。該方法直接以二維信息為研究對象避免了一維流形算法需將二維信息轉(zhuǎn)化為向量帶來的信息損失,與一般PCA方法相比更能發(fā)現(xiàn)隱藏在高維數(shù)據(jù)流形結(jié)構(gòu)中的局部數(shù)據(jù)特征。工程信號分析驗(yàn)證了該方法的有效性。流形奇異值熵與概率神經(jīng)網(wǎng)絡(luò)結(jié)合應(yīng)用,進(jìn)一步證實(shí)了該方法具有很高的可靠性。 4.針對工程實(shí)際操作與應(yīng)用問題,開發(fā)了一套滾動軸承故障振動分析系統(tǒng)。將信號處理技術(shù)、故障診斷技術(shù)、數(shù)據(jù)庫知識、虛擬儀器技術(shù)及人機(jī)交互技術(shù)相結(jié)合,采用遞進(jìn)式分模塊進(jìn)行研制,滿足了工程現(xiàn)場方便快捷實(shí)用的需求。
[Abstract]:As one of the key and fault prone parts in rotating machinery equipment, the running state of rolling bearings directly affects the performance of the whole equipment system, so it is of great significance to study the status identification and fault diagnosis of rolling bearings. Signal de-noising and fault feature extraction are the core contents in state identification and fault diagnosis. Because of the complex running environment of rolling bearing, the disturbance is much, the noise is large, and the fault signal is mostly non-stationary and nonlinear signal. Thus, the effectiveness of the traditional methods of noise reduction and fault feature extraction is greatly reduced. Therefore, in this paper, the rolling bearing is taken as the research object. According to the characteristics of its early fault vibration signal under strong background noise, the manifold method is combined with other modern vibration signal analysis methods. The noise reduction and feature extraction of rolling bearing fault signals are studied in depth. The main contents of the thesis are as follows: 1. The background significance of the topic and the development process of rolling bearing fault diagnosis at home and abroad are discussed, and the two key problems of noise reduction and fault feature extraction in contemporary rolling bearing fault diagnosis are summarized in detail. This paper analyzes the common fault forms of rolling bearings and their causes and consequences, and expounds the vibration mechanism of rolling bearings and the concrete application methods of the theory of vibration analysis in time domain, frequency domain and time and frequency domain. 2. Aiming at the problem that the early weak fault characteristics of rolling bearings are easily submerged by noise and interference components, a new method for reducing noise of early weak fault signals based on improved EMD is proposed by combining KPCA manifold, EMD and LTSA manifold. First, KPCA manifold denoising is performed before EMD decomposition, then all IMF coefficients are extracted by LTSA manifold algorithm on the basis of EMD decomposition, and the new signal is obtained by the sum of IMF coefficients, which can be used to reduce noise. This method not only makes full use of the advantage of EMD complete adaptation to analyze nonstationary nonlinear signals, but also effectively overcomes the effect of noise on EMD decomposition. The problem of losing fault information caused by neglecting most components in conventional EMD applications is well solved. The effectiveness and superiority of the method are verified by comparing and analyzing the actual signals in engineering. 3. To solve the problem of how to effectively extract the sensitive features of non-stationary nonlinear fault signals, a novel time-frequency fault feature extraction method for rolling bearings based on two-dimensional manifold and Hilbert time-frequency spectrum is proposed. On the basis of Hilbert time-frequency analysis, the two-dimensional LPP manifold algorithm based on improved LPP algorithm is applied to extract the signal flow formation fraction, and then the singular value entropy is defined to quantitatively measure the difference of manifold characteristics in different fault states. This method takes two-dimensional information as the research object directly and avoids the information loss caused by the one-dimensional manifold algorithm which needs to transform the two-dimensional information into vectors. Compared with the general PCA method, it can find the local data features hidden in the high-dimensional data stream shape structure. The engineering signal analysis verifies the effectiveness of the method. The application of Manifold singular value entropy and probabilistic neural network further proves that this method is highly reliable. 4. A rolling bearing fault vibration analysis system is developed for practical operation and application. The signal processing technology, fault diagnosis technology, database knowledge, virtual instrument technology and human-computer interaction technology are combined to develop the progressive sub-module.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TH133.33;TH165.3
本文編號：2422281
[Abstract]:As one of the key and fault prone parts in rotating machinery equipment, the running state of rolling bearings directly affects the performance of the whole equipment system, so it is of great significance to study the status identification and fault diagnosis of rolling bearings. Signal de-noising and fault feature extraction are the core contents in state identification and fault diagnosis. Because of the complex running environment of rolling bearing, the disturbance is much, the noise is large, and the fault signal is mostly non-stationary and nonlinear signal. Thus, the effectiveness of the traditional methods of noise reduction and fault feature extraction is greatly reduced. Therefore, in this paper, the rolling bearing is taken as the research object. According to the characteristics of its early fault vibration signal under strong background noise, the manifold method is combined with other modern vibration signal analysis methods. The noise reduction and feature extraction of rolling bearing fault signals are studied in depth. The main contents of the thesis are as follows: 1. The background significance of the topic and the development process of rolling bearing fault diagnosis at home and abroad are discussed, and the two key problems of noise reduction and fault feature extraction in contemporary rolling bearing fault diagnosis are summarized in detail. This paper analyzes the common fault forms of rolling bearings and their causes and consequences, and expounds the vibration mechanism of rolling bearings and the concrete application methods of the theory of vibration analysis in time domain, frequency domain and time and frequency domain. 2. Aiming at the problem that the early weak fault characteristics of rolling bearings are easily submerged by noise and interference components, a new method for reducing noise of early weak fault signals based on improved EMD is proposed by combining KPCA manifold, EMD and LTSA manifold. First, KPCA manifold denoising is performed before EMD decomposition, then all IMF coefficients are extracted by LTSA manifold algorithm on the basis of EMD decomposition, and the new signal is obtained by the sum of IMF coefficients, which can be used to reduce noise. This method not only makes full use of the advantage of EMD complete adaptation to analyze nonstationary nonlinear signals, but also effectively overcomes the effect of noise on EMD decomposition. The problem of losing fault information caused by neglecting most components in conventional EMD applications is well solved. The effectiveness and superiority of the method are verified by comparing and analyzing the actual signals in engineering. 3. To solve the problem of how to effectively extract the sensitive features of non-stationary nonlinear fault signals, a novel time-frequency fault feature extraction method for rolling bearings based on two-dimensional manifold and Hilbert time-frequency spectrum is proposed. On the basis of Hilbert time-frequency analysis, the two-dimensional LPP manifold algorithm based on improved LPP algorithm is applied to extract the signal flow formation fraction, and then the singular value entropy is defined to quantitatively measure the difference of manifold characteristics in different fault states. This method takes two-dimensional information as the research object directly and avoids the information loss caused by the one-dimensional manifold algorithm which needs to transform the two-dimensional information into vectors. Compared with the general PCA method, it can find the local data features hidden in the high-dimensional data stream shape structure. The engineering signal analysis verifies the effectiveness of the method. The application of Manifold singular value entropy and probabilistic neural network further proves that this method is highly reliable. 4. A rolling bearing fault vibration analysis system is developed for practical operation and application. The signal processing technology, fault diagnosis technology, database knowledge, virtual instrument technology and human-computer interaction technology are combined to develop the progressive sub-module.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TH133.33;TH165.3
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