發(fā)布時(shí)間：2018-05-27 03:07

  本文選題：包絡(luò)分析 + 同步平均　； 參考：《昆明理工大學(xué)》2013年碩士論文

【摘要】：齒輪為旋轉(zhuǎn)機(jī)械傳遞動(dòng)力、改變轉(zhuǎn)速的重要零件之一,同時(shí)也是旋轉(zhuǎn)機(jī)械中最容易發(fā)生機(jī)械故障的典型零件之一。齒輪振動(dòng)信號(hào)由于存在傳播路徑復(fù)雜,且信號(hào)源距離傳感器安裝位置較遠(yuǎn)等問題,分析通常較為困難,較難準(zhǔn)確判斷故障發(fā)生與否。當(dāng)故障嚴(yán)重時(shí),不僅影響生產(chǎn),甚至?xí)䦟θ说纳踩斐赏{。此外,目前對齒輪故障診斷技術(shù)的研究大多是針對平穩(wěn)振動(dòng)信號(hào),而對升、降速等非平穩(wěn)過程的故障特征提取研究相對較少,因此,亟需完善非平穩(wěn)工況下的齒輪故障診斷技術(shù)。 振動(dòng)信號(hào)特征分析是機(jī)械故障診斷中較為常用的技術(shù)方法,通過對振動(dòng)信號(hào)特征的提取可以有效判斷機(jī)械是否存在故障。在基于振動(dòng)信號(hào)特征分析的齒輪故障診斷領(lǐng)域,已不斷發(fā)展更為有效的信號(hào)處理手段；如何從受復(fù)雜背景噪聲干擾的振動(dòng)信號(hào)中準(zhǔn)確提取出齒輪故障特征信息,是目前齒輪故障診斷領(lǐng)域的研究熱點(diǎn),這也是論文探討和研究的主要內(nèi)容。 本論文就齒輪的典型故障進(jìn)行了深入的分析研究,在總結(jié)前人研究成果的基礎(chǔ)上,提出了包絡(luò)同步平均的齒輪故障診斷技術(shù)。首先利用譜峭度算法獲得最優(yōu)的共振解調(diào)參數(shù),提取時(shí)域復(fù)包絡(luò)信號(hào),突出其中的沖擊成分；然后將時(shí)域復(fù)包絡(luò)信號(hào)進(jìn)行角域轉(zhuǎn)換,并選取對應(yīng)的參考軸,對復(fù)包絡(luò)角域信號(hào)的實(shí)部和虛部分別進(jìn)行同步平均;最后通過階比譜圖提取其故障特征。 同時(shí),論文針對齒輪變速過程中的故障特征提取進(jìn)行了研究,對于升、降速階段的齒輪振動(dòng)信號(hào),利用階比跟蹤技術(shù),把信號(hào)轉(zhuǎn)換到角域進(jìn)行變速過程的齒輪故障特征信息的提取。研究中提出將包絡(luò)分析與角域同步平均技術(shù)相結(jié)合,依次選取不同的轉(zhuǎn)軸作為參考軸,可得到不同轉(zhuǎn)軸上齒輪的故障特征,有效解決了多齒輪典型故障特征難以分離的問題。該方法可有效消除原包絡(luò)信號(hào)中的寬帶噪聲干擾,分離出與故障齒輪所在軸有關(guān)的階比分量,同時(shí)可克服轉(zhuǎn)速波動(dòng)對信號(hào)分析產(chǎn)生的頻率模糊現(xiàn)象。 配合上述理論研究,在論文研究中進(jìn)行了仿真和實(shí)際測試研究,仿真和實(shí)測試驗(yàn)結(jié)果驗(yàn)證了所提出方法的有效性。
[Abstract]:Gear is one of the most important parts for rotating machinery to transfer power and change rotational speed. It is also one of the typical parts of rotating machinery which is prone to mechanical failure. Due to the complex propagation path and the distance between the signal source and the sensor, the analysis of gear vibration signal is usually difficult, and it is difficult to accurately judge whether the fault occurs or not. When the fault is serious, it will not only affect the production, but also threaten the safety of human life. In addition, most of the researches on gear fault diagnosis are aimed at stationary vibration signals, but the research on fault feature extraction of non-stationary processes such as rising and decelerating is relatively few. It is urgent to improve the gear fault diagnosis technology under non-stationary working conditions. Vibration signal feature analysis is a common technique in mechanical fault diagnosis. The feature extraction of vibration signal can effectively judge whether machinery has fault or not. In the field of gear fault diagnosis based on vibration signal characteristic analysis, more effective signal processing methods have been developed, how to extract gear fault characteristic information from vibration signal interfered by complex background noise accurately, It is a hot spot in the field of gear fault diagnosis, which is also the main content of this paper. In this paper, the typical faults of gears are deeply analyzed and studied. Based on the previous research results, a fault diagnosis technology of gear with envelope synchronous average is proposed. Firstly, the optimal resonance demodulation parameters are obtained by spectral kurtosis algorithm, and the time domain complex envelope signal is extracted to highlight the impact components, and then the time domain complex envelope signal is converted into angular domain, and the corresponding reference axis is selected. The real part and the imaginary part of the complex envelope angle domain signal are synchronously averaged, and the fault characteristics are extracted by order spectrum. At the same time, the paper studies the fault feature extraction in the gear speed changing process. For the gear vibration signal in the rising and falling stages, the order tracking technique is used. The gear fault feature information is extracted by converting the signal to the angle domain. In the study, it is proposed that the fault characteristics of gears on different rotating shafts can be obtained by combining the envelope analysis with the angular domain synchronous averaging technique and selecting different rotating shafts as reference shafts in turn, which effectively solves the problem that the typical fault features of multiple gears are difficult to separate. This method can effectively eliminate the wideband noise interference in the original envelope signal and separate the order component related to the shaft of the fault gear. At the same time, it can overcome the frequency ambiguity caused by the speed fluctuation on the signal analysis. With the above theoretical research, the simulation and practical test are carried out in this paper. The results of simulation and actual test verify the effectiveness of the proposed method.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TH132.41;TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李輝;鄭海起;潘宏俠;;基于階次跟蹤和角域平均的齒輪裂紋故障診斷[J];中北大學(xué)學(xué)報(bào)(自然科學(xué)版);2007年01期

2 陳進(jìn);廖明夫;陳剛;;信號(hào)周期分段處理法診斷單級(jí)傳動(dòng)齒輪副故障[J];航空動(dòng)力學(xué)報(bào);2006年04期

3 林京,屈梁生;信號(hào)時(shí)頻熵及其在齒輪裂紋識(shí)別中的應(yīng)用[J];機(jī)械傳動(dòng);1998年02期

4 張周鎖,李凌均,何正嘉;基于支持向量機(jī)的多故障分類器及應(yīng)用[J];機(jī)械科學(xué)與技術(shù);2004年05期

5 韓捷;李軍偉;李志農(nóng);;階比雙譜及其在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用[J];機(jī)械強(qiáng)度;2006年06期

6 郭瑜,秦樹人,湯寶平,紀(jì)躍波;基于瞬時(shí)頻率估計(jì)的旋轉(zhuǎn)機(jī)械階比跟蹤[J];機(jī)械工程學(xué)報(bào);2003年03期

7 郭瑜,秦樹人;無轉(zhuǎn)速計(jì)旋轉(zhuǎn)機(jī)械升降速振動(dòng)信號(hào)零相位階比跟蹤濾波[J];機(jī)械工程學(xué)報(bào);2004年03期

8 鐘群鵬;張崢;有移亮;;我國安全生產(chǎn)(含安全制造)的科學(xué)發(fā)展若干問題[J];機(jī)械工程學(xué)報(bào);2007年01期

9 郭瑜,羅德?lián)P;基于瞬時(shí)頻率估計(jì)的瀑布圖分析[J];昆明理工大學(xué)學(xué)報(bào)(理工版);2005年03期

10 熊國良;張龍;;變工況下轉(zhuǎn)子振動(dòng)信號(hào)的時(shí)頻分析方法比較[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2008年07期

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