本文選題：旋轉(zhuǎn)機械 + 特征提取�。� 參考：《昆明理工大學》2011年碩士論文

【摘要】：旋轉(zhuǎn)機械是工業(yè)生產(chǎn)部門中廣泛應用的一類機械設備,是生產(chǎn)部門的核心設備,例如發(fā)電機、壓縮機、汽輪機、鼓風機等機械設備都屬于這一類。隨著現(xiàn)代科學技術(shù)的迅猛發(fā)展和機械設備日趨向大型化、高速化、集成化、功能越來越多、結(jié)構(gòu)越來越復雜、自動化程度越來越高,由此產(chǎn)生對旋轉(zhuǎn)機械設備的管理以及維護要求也越來越高。此類設備能否正常運行,對生產(chǎn)部門和國民經(jīng)濟具有重要的意義。 本文以旋轉(zhuǎn)機械非平穩(wěn)振動信號為研究對象,以盲源分離、包絡分析和階比分析為研究手段,研究對故障的提取和分離新途徑,實現(xiàn)對齒輪故障和軸承故障特征提取以及同時存在兩種故障的有效分離。 論文分別對同時存在齒輪斷齒故障和軸承外圈故障、內(nèi)圈故障以及和滾動體故障的情況下進行了試驗研究。提出了針對齒輪箱的多故障源分離方法,首先利用包絡分析提取含有多種故障的特征信息的包絡波形,然后結(jié)合獨立分量分析,從混合信號中分離出信號中的獨立源分量,最后在各獨立分量中獲得其對應的故障源,成功實現(xiàn)在復合故障下的故障特征提取與故障源的有效分離。 研究中,在對獨立分量分析技術(shù)進行了深入掌握的基礎(chǔ)上,結(jié)合階比包絡譜分析技術(shù),提出了“基于獨立分量分析與包絡階比分析的齒輪箱多振源特征提取”方法。該方法解決了獨立振源數(shù)目一般并不能預先確定,直接應用獨立分量分析方法往往并不能實現(xiàn)對混合信號的有效分離問題,采用包絡提取實現(xiàn)對原信號中振源數(shù)的降維,然后對包絡波形進行階比跟蹤等角度采樣,對等角度采樣信號應用獨立分量分析進行按源分離和包絡階比分析,提取出各振源的振動特征。研究表明該方法可實現(xiàn)對滾動軸承外圈故障和齒輪斷齒故障的特征提取和分離,對旋轉(zhuǎn)機械多源故障條件下特征提取技術(shù)的發(fā)展有較好的學術(shù)研究意義。
[Abstract]:Rotating machinery is a kind of machinery widely used in industrial production department. It is the core equipment of production department, such as generator, compressor, steam turbine, blower and so on.With the rapid development of modern science and technology and the increasing trend of machinery and equipment to large-scale, high-speed, integrated, more and more functions, more and more complex structure, the degree of automation is becoming higher and higher.As a result, the management and maintenance of rotating machinery are becoming more and more important.This kind of equipment can run normally, to production department and national economy have important meaning.In this paper, the non-stationary vibration signals of rotating machinery are taken as the research object, and the blind source separation, envelope analysis and order ratio analysis are taken as the research means to study the new ways of fault extraction and separation.The gear fault and bearing fault feature extraction are realized and the two faults are separated effectively.In this paper, the fault of gear tooth break and bearing outer ring, inner ring and rolling body are studied respectively.In this paper, a multi-fault source separation method for gearbox is proposed. Firstly, envelope analysis is used to extract the envelope waveform with the characteristic information of various faults, and then the independent source component is separated from the mixed signal by using the independent component analysis (ICA).Finally, the corresponding fault source is obtained in each independent component, and the fault feature extraction and the effective separation of fault source are successfully realized.In the research, on the basis of deeply mastering the independent component analysis technology and combining the order ratio envelope spectrum analysis technique, the method of "extracting the feature of multi-vibration source of gearbox based on independent component analysis and envelope order analysis" is put forward.This method solves the problem that the number of independent vibration sources can not be determined in advance, the direct application of independent component analysis method can not realize the effective separation of mixed signals, and the dimensionality reduction of the number of vibration sources in the original signal can be realized by using envelope extraction.Then the envelope waveform is sampled from different angles such as order tracking. The independent component analysis (ICA) is used to separate the envelope waveform and the envelope order ratio analysis is used to extract the vibration characteristics of each vibration source.The results show that this method can be used to extract and separate the features of the outer ring fault of rolling bearing and the fault of gear broken tooth, and it has a good academic significance for the development of feature extraction technology under the condition of multi-source fault of rotating machinery.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

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