本文選題：滾動(dòng)軸承 + 故障診斷��； 參考：《大連理工大學(xué)》2013年博士論文

【摘要】：滾動(dòng)軸承是旋轉(zhuǎn)機(jī)械中最常用的零部件之一,其工作狀態(tài)正常與否直接影響到整臺(tái)設(shè)備的性能甚至整個(gè)生產(chǎn)線(xiàn)的安全和生產(chǎn)。因此,研究滾動(dòng)軸承的狀態(tài)監(jiān)測(cè)和故障診斷技術(shù),對(duì)于避免突發(fā)事故的發(fā)生以及維修體制的變革等具有重要的理論價(jià)值和現(xiàn)實(shí)意義。本文以滾動(dòng)軸承為研究對(duì)象,針對(duì)特征提取這一滾動(dòng)軸承故障診斷中的關(guān)鍵問(wèn)題,從軸承振動(dòng)信號(hào)的處理著手,進(jìn)行了一系列的研究工作。主要內(nèi)容如下： 1)闡述了論文選題的背景與意義,分析了滾動(dòng)軸承的振動(dòng)機(jī)理,總結(jié)了滾動(dòng)軸承診斷信息獲取、故障特征提取和故障模式識(shí)別等方面國(guó)內(nèi)外的研究現(xiàn)狀,在此基礎(chǔ)之上,確立了本文的研究思路和研究?jī)?nèi)容。 2)提出了一種基于IMF峭度的滾動(dòng)軸承故障診斷方法。在分析峭度在軸承故障診斷中的局限性的基礎(chǔ)上,提出利用經(jīng)驗(yàn)?zāi)Ｊ椒纸?EMD)方法把滾動(dòng)軸承的振動(dòng)信號(hào)分解為一系列代表不同頻帶的‘內(nèi)蘊(yùn)模式函數(shù)(IMF),再取包含豐富故障信息的前幾個(gè)IMF計(jì)算其峭度值,用不同頻帶信號(hào)的峭度值來(lái)進(jìn)行軸承工作狀態(tài)的分析,最后以這些峭度值作為故障特征向量,輸入支持向量機(jī)(SVM)實(shí)現(xiàn)軸承的故障診斷,并用實(shí)驗(yàn)信號(hào)進(jìn)行了驗(yàn)證。 3)針對(duì)IMF峭度對(duì)軸承故障損傷程度識(shí)別能力的不足,提出了一種基于IMF包絡(luò)樣本熵的滾動(dòng)軸承故障診斷方法。首先介紹了熵概念的發(fā)展及泛化,并給出了樣本熵的定義,然后針對(duì)樣本熵在表征信號(hào)復(fù)雜度時(shí)存在熵值大小和復(fù)雜度高低不一致的缺點(diǎn),結(jié)合滾動(dòng)軸承故障信號(hào)的調(diào)制特征,提出利用包絡(luò)信號(hào)的樣本熵作為滾動(dòng)軸承的故障特征。該方法利用EMD先把振動(dòng)信號(hào)分解為若干IMF之和,再選取包含豐富故障信息的IMF求其包絡(luò)信號(hào),最后計(jì)算包絡(luò)信號(hào)的樣本熵,然后以包絡(luò)樣本熵組成故障特征向量,結(jié)合SVM完成滾動(dòng)軸承不同工作狀態(tài)的識(shí)別。結(jié)果表明,所提方法不僅能夠區(qū)分軸承不同類(lèi)型的故障,還能準(zhǔn)確識(shí)別不同的故障損傷程度。 4)將層次熵(Hierarchical entropy,HE)引入到滾動(dòng)軸承故障特征提取中。在分析樣本熵和多尺度熵表征信號(hào)復(fù)雜度的不足的基礎(chǔ)上,介紹了層次熵的基本概念和算法,提出了基于層次熵的滾動(dòng)軸承故障特征提取方法。層次熵通過(guò)構(gòu)造特定的算子,不但能夠考察信號(hào)“低頻成分”的樣本熵,還能夠計(jì)算“高頻成分”的樣本熵。最后,結(jié)合SVM實(shí)現(xiàn)滾動(dòng)軸承的故障診斷,同時(shí)與前面提出的故障診斷方法以及多尺度熵方法進(jìn)行了對(duì)比分析。實(shí)驗(yàn)驗(yàn)證結(jié)果表明,基于層次熵和SVM的滾動(dòng)軸承故障診斷方法效果最優(yōu),識(shí)別率達(dá)到了100%。 5)提出了一種基于EMD和相關(guān)系數(shù)的滾動(dòng)軸承早期故障檢測(cè)方法。運(yùn)用模式識(shí)別方法進(jìn)行不同軸承故障類(lèi)型的分類(lèi)是建立在故障樣本數(shù)據(jù)可獲得的基礎(chǔ)之上的,而在實(shí)際的生產(chǎn)應(yīng)用中,很難獲得不同故障類(lèi)型的軸承振動(dòng)數(shù)據(jù)。針對(duì)這一問(wèn)題,把軸承故障檢測(cè)當(dāng)作是異常檢測(cè)問(wèn)題,完全基于軸承正常振動(dòng)信號(hào),通過(guò)相關(guān)系數(shù)的計(jì)算和簡(jiǎn)單的設(shè)定,實(shí)現(xiàn)軸承故障的自動(dòng)檢測(cè)。完成故障的檢測(cè)以后,利用包絡(luò)分析法完成軸承故障的診斷,最后通過(guò)全壽命周期實(shí)驗(yàn)信號(hào)驗(yàn)證了該方法的有效性。 6)將希爾伯特振動(dòng)分解(Hilbert Vibration Decomposition,HVD)引入到滾動(dòng)軸承的故障診斷中。在分析EMD模態(tài)混疊現(xiàn)象的基礎(chǔ)上,介紹了HVD的基本原理和性質(zhì),并用仿真信號(hào)對(duì)比分析了HVD和EMD在分解含有異常事件的信號(hào)時(shí)的分解效果。然后,提出了基于HVD的滾動(dòng)軸承故障診斷方法。該方法首先運(yùn)用HVD對(duì)軸承振動(dòng)信號(hào)進(jìn)行分解,再結(jié)合包絡(luò)分析實(shí)現(xiàn)軸承的故障診斷,實(shí)驗(yàn)信號(hào)的分析結(jié)果表明該方法能夠有效地進(jìn)行滾動(dòng)軸承的故障診斷。
[Abstract]:Rolling bearing is one of the most commonly used parts in rotating machinery. Its normal working condition directly affects the performance of the whole equipment and even the safety and production of the whole production line. Therefore, it is important to study the state monitoring and fault diagnosis technology of the rolling bearings to avoid the occurrence of sudden accidents and the reform of the maintenance system. The paper takes the rolling bearing as the research object, and aims at the key problems in the fault diagnosis of the rolling bearing, and carries out a series of research work from the processing of the bearing vibration signal. The main contents are as follows:
1) this paper expounds the background and significance of the topic selection, analyzes the vibration mechanism of the rolling bearing, summarizes the research status of the diagnosis information acquisition, fault feature extraction and fault pattern recognition of rolling bearings, and based on this, establishes the research ideas and research contents of this paper.
2) a fault diagnosis method for rolling bearings based on IMF kurtosis is proposed. On the basis of analyzing the limitation of kurtosis in bearing fault diagnosis, the method of empirical mode decomposition (EMD) is proposed to decompose the vibration signals of rolling bearings into a series of 'intrinsic mode functions (IMF) "representing different frequency bands, and then contains rich fault information. The first few IMF calculated its kurtosis value and analyzed the working state of bearing with the kurtosis value of different frequency band signals. Finally, these kurtosis values were used as fault feature vectors, and the support vector machine (SVM) was input to realize the fault diagnosis of bearing, and the experimental signal was used to verify it.
3) in view of the lack of IMF kurtosis to identify the bearing fault damage degree, a fault diagnosis method based on IMF envelope sample entropy is proposed. Firstly, the development and generalization of entropy concept are introduced, and the definition of sample entropy is given. Then, the entropy value and complexity of the sample entropy are used to characterize the number and complexity of the letter number complexity. In this method, the sample entropy of the envelope signal is used as the fault feature of the rolling bearing. This method uses the EMD to decompose the vibration signal into the sum of several IMF, and then selects the IMF containing the rich fault information to obtain the envelope signal, and finally calculates the sample entropy of the envelope signal. Then, the sample entropy of the envelope signal is calculated, and then the sample entropy of the envelope signal is calculated. Then, the sample entropy of the envelope signal is calculated, and then the sample entropy is calculated. Then, the sample entropy of the envelope signal is calculated, then the sample entropy is calculated. Then, the sample entropy of the envelope signal is calculated. The fault feature vectors are composed of envelope sample entropy and SVM is used to identify different working states of rolling bearings. The results show that the proposed method can not only distinguish different types of fault of bearing, but also identify different degree of fault damage accurately.
4) Hierarchical entropy (HE) is introduced into the fault feature extraction of rolling bearings. Based on the analysis of the shortage of sample entropy and multi-scale entropy, the basic concepts and algorithms of hierarchical entropy are introduced, and the method of extracting the fault characteristics of rolling bearings based on hierarchical entropy is proposed. It can not only examine the sample entropy of the signal "low frequency components", but also calculate the sample entropy of the "high frequency component". Finally, the fault diagnosis of rolling bearings is realized with SVM. At the same time, it is compared with the previous fault diagnosis method and the multi-scale entropy method. The verification results show that the rolling based on the level entropy and the SVM are rolling. The bearing fault diagnosis method has the best effect and the recognition rate reaches 100%.
5) an early fault detection method of rolling bearing based on EMD and correlation coefficient is proposed. The classification of different bearing fault types using pattern recognition method is based on the data obtained from the fault sample data. In actual production application, it is difficult to obtain the bearing vibration data with different types of fault. The problem of bearing fault detection is regarded as an anomaly detection problem. It is based on the normal vibration signal of the bearing. The bearing fault detection is realized by the calculation of the correlation coefficient and the simple setting. After the fault detection is completed, the diagnosis of the bearing fault is completed by the envelope analysis method. Finally, the whole life cycle experimental signal is verified. The effectiveness of the method.
6) the Hilbert vibration decomposition (Hilbert Vibration Decomposition, HVD) is introduced into the fault diagnosis of rolling bearings. Based on the analysis of EMD modal aliasing, the basic principles and properties of HVD are introduced, and the decomposition effects of HVD and EMD in the decomposition of signals containing abnormal events are compared and analyzed by the simulation signals. The method of fault diagnosis of rolling bearing based on HVD. This method first decomposes the vibration signal of bearing by using HVD, and then combines envelope analysis to realize the fault diagnosis of bearing. The analysis result of experimental signal shows that the method can effectively diagnose the fault of rolling bearing.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TH133.33;TH165.3

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