基于盲源分離的滾動軸承復合故障診斷方法研究
發(fā)布時間：2018-08-14 16:07
【摘要】：滾動軸承作為一種承受和傳遞載荷的重要部件,在機械設(shè)備中得到了廣泛應(yīng)用,其運行狀態(tài)會直接影響設(shè)備整體性能,滾動軸承部件一旦發(fā)生故障,可能導致設(shè)備損毀,甚至造成災(zāi)難性事故。因此,對滾動軸承進行故障診斷具有非常重要的意義。在實際生產(chǎn)環(huán)境中,滾動軸承某一部位出現(xiàn)故障時常伴隨著其它部位的故障,即出現(xiàn)滾動軸承的復合故障。這種情況下多個故障的振動源信號及噪聲相互耦合,振動信號呈現(xiàn)復雜化,故障類型的診斷變得尤為困難。而盲源分離是解決復合故障源信號分離問題的有效方法之一。因此,論文以滾動軸承復合故障振動信號為研究對象,結(jié)合盲源分離理論以及時頻分析和模式識別方法,對滾動軸承復合故障診斷過程中遇到的常規(guī)和極端條件下的故障源分離、特征提取、故障類型診斷等問題展開研究。論文的主要研究工作可概述如下:對盲源分離方法的基本理論進行研究,并通過仿真實驗對幾種經(jīng)典盲源分離算法的分離效果進行分析比較,在此基礎(chǔ)上,將JADE盲源分離算法用于分離滾動軸承復合故障信號。滾動軸承故障信號是非平穩(wěn)和非線性的,而傳統(tǒng)的時域和頻域信號分析方法難以兼顧非平穩(wěn)信號的時變特性,不能準確體現(xiàn)滾動軸承各個故障類型的故障特性。針對這一問題,提出一種基于EMD時頻分析的樣本熵和能量比特征提取方法,該方法在時頻分析基礎(chǔ)上進行信號特征提取,能夠更全面、更準確地揭示滾動軸承振動信號中的故障特征信息。此外,結(jié)合盲源信號分離、特征提取以及支持向量機構(gòu)建了一種有效的滾動軸承復合故障診斷機制。傳統(tǒng)的盲源分離方法大多基于觀測信號數(shù)不少于源信號數(shù)的假設(shè),不能適應(yīng)于單通道條件下的復合故障信號分離。因此,將變分模態(tài)分解方法引入到盲源分離領(lǐng)域,通過變分模態(tài)分解將單通道盲源分離的極端欠定問題轉(zhuǎn)化為適定或超定問題,為單通道條件下的復合故障診斷實現(xiàn)提供一種有效的解決方案。實驗結(jié)果表明,提出的基于VMD的單通道盲源分離方法相對于傳統(tǒng)方法更具優(yōu)越性。
[Abstract]:As an important component to bear and transfer load, rolling bearing has been widely used in mechanical equipment. Its running state will directly affect the overall performance of the equipment. Once the rolling bearing component fails, it may lead to equipment damage. And even catastrophic accidents. Therefore, the fault diagnosis of rolling bearings is of great significance. In the actual production environment, the fault of one part of the rolling bearing is often accompanied by the fault of other parts, that is, the compound fault of the rolling bearing. In this case, the signal of vibration source and noise of multiple faults are coupled with each other, the vibration signal is complicated, and the diagnosis of fault type becomes more and more difficult. Blind source separation is one of the effective methods to solve the problem of complex fault source signal separation. Therefore, the paper takes the complex fault vibration signal of rolling bearing as the research object, and combines the blind source separation theory with the method of timely frequency analysis and pattern recognition. The problems of fault source separation, feature extraction, fault type diagnosis and so on in the process of rolling bearing composite fault diagnosis are studied. The main research work of this paper can be summarized as follows: the basic theory of blind source separation method is studied, and the separation effect of several classical blind source separation algorithms is analyzed and compared by simulation experiments. JADE blind source separation algorithm is used to separate composite fault signals of rolling bearings. The fault signals of rolling bearings are non-stationary and nonlinear, but the traditional time-domain and frequency-domain signal analysis methods are difficult to take into account the time-varying characteristics of non-stationary signals and can not accurately reflect the fault characteristics of each fault type of rolling bearings. In order to solve this problem, a method of extracting feature of sample entropy and energy ratio based on EMD time-frequency analysis is proposed. The fault characteristic information in the vibration signal of rolling bearing is revealed more accurately. In addition, combining blind source signal separation, feature extraction and support vector mechanism, an effective fault diagnosis mechanism for rolling bearing is proposed. Most of the traditional blind source separation methods are based on the assumption that the number of observed signals is not less than the number of source signals. Therefore, the variational mode decomposition method is introduced into the field of blind source separation, and the extreme underdetermination problem of single channel blind source separation is transformed into a suitable or overdetermined problem by variational mode decomposition. It provides an effective solution for the realization of complex fault diagnosis under the condition of single channel. Experimental results show that the proposed single channel blind source separation method based on VMD is superior to the traditional method.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TH133.33
[Abstract]:As an important component to bear and transfer load, rolling bearing has been widely used in mechanical equipment. Its running state will directly affect the overall performance of the equipment. Once the rolling bearing component fails, it may lead to equipment damage. And even catastrophic accidents. Therefore, the fault diagnosis of rolling bearings is of great significance. In the actual production environment, the fault of one part of the rolling bearing is often accompanied by the fault of other parts, that is, the compound fault of the rolling bearing. In this case, the signal of vibration source and noise of multiple faults are coupled with each other, the vibration signal is complicated, and the diagnosis of fault type becomes more and more difficult. Blind source separation is one of the effective methods to solve the problem of complex fault source signal separation. Therefore, the paper takes the complex fault vibration signal of rolling bearing as the research object, and combines the blind source separation theory with the method of timely frequency analysis and pattern recognition. The problems of fault source separation, feature extraction, fault type diagnosis and so on in the process of rolling bearing composite fault diagnosis are studied. The main research work of this paper can be summarized as follows: the basic theory of blind source separation method is studied, and the separation effect of several classical blind source separation algorithms is analyzed and compared by simulation experiments. JADE blind source separation algorithm is used to separate composite fault signals of rolling bearings. The fault signals of rolling bearings are non-stationary and nonlinear, but the traditional time-domain and frequency-domain signal analysis methods are difficult to take into account the time-varying characteristics of non-stationary signals and can not accurately reflect the fault characteristics of each fault type of rolling bearings. In order to solve this problem, a method of extracting feature of sample entropy and energy ratio based on EMD time-frequency analysis is proposed. The fault characteristic information in the vibration signal of rolling bearing is revealed more accurately. In addition, combining blind source signal separation, feature extraction and support vector mechanism, an effective fault diagnosis mechanism for rolling bearing is proposed. Most of the traditional blind source separation methods are based on the assumption that the number of observed signals is not less than the number of source signals. Therefore, the variational mode decomposition method is introduced into the field of blind source separation, and the extreme underdetermination problem of single channel blind source separation is transformed into a suitable or overdetermined problem by variational mode decomposition. It provides an effective solution for the realization of complex fault diagnosis under the condition of single channel. Experimental results show that the proposed single channel blind source separation method based on VMD is superior to the traditional method.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TH133.33
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