本文選題：故障診斷　切入點(diǎn)：特征指標(biāo)　出處：《北京交通大學(xué)》2011年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：摘要：設(shè)備狀態(tài)監(jiān)測(cè)與故障診斷技術(shù)是一種了解和掌握設(shè)備在使用過(guò)程中的狀態(tài),對(duì)設(shè)備運(yùn)行的狀態(tài)異常進(jìn)行監(jiān)測(cè)并對(duì)故障類(lèi)型進(jìn)行判斷的技術(shù)。作者所在團(tuán)隊(duì)在服務(wù)于神華集團(tuán)準(zhǔn)格爾露天煤礦礦鏟設(shè)備的故障診斷項(xiàng)目時(shí)發(fā)現(xiàn)：作為礦用設(shè)備代表的礦用電鏟,其工作條件有環(huán)境惡劣、工況復(fù)雜變化,工作模式多樣等特點(diǎn)。其中滾動(dòng)軸承承受的壓力最大,長(zhǎng)期工作于變轉(zhuǎn)速的狀態(tài),最容易發(fā)生損壞。因此,研究能夠適應(yīng)變轉(zhuǎn)速條件的軸承故障診斷技術(shù)非常必要的。 本文的主要研究?jī)?nèi)容與工作如下： 首先,對(duì)滾動(dòng)軸承的失效形式及其故障診斷方法進(jìn)行了分析,通過(guò)分析時(shí)域特征參數(shù)對(duì)于故障信息的敏感程度,提取了峭度指標(biāo)等作為本文故障診斷的有效特征參數(shù)。 其次,對(duì)軸承轉(zhuǎn)速改變的情況下的振動(dòng)信號(hào)進(jìn)行分析。因?yàn)榍投戎笜?biāo)等無(wú)法完成振動(dòng)波形復(fù)雜度表征,所以加入了近似熵和功率譜熵作為補(bǔ)充,進(jìn)而提出了融合多參數(shù)的滾動(dòng)軸承故障特征提取方法。通過(guò)求待檢測(cè)波形相對(duì)于特征參數(shù)指標(biāo)的距,確定故障類(lèi)別和故障程度。此方法不僅能較準(zhǔn)確的分辨出故障類(lèi)型,而且能夠避免轉(zhuǎn)速對(duì)故障診斷結(jié)果的影響,經(jīng)試驗(yàn)證實(shí)是切實(shí)可行有效的。 再次,分別對(duì)三維特征指標(biāo)距和六維特征指標(biāo)距的診斷效果做了對(duì)比分析,從理論上分析了維數(shù)的增加對(duì)故障診斷效果的影響并利用試驗(yàn)加以驗(yàn)證。提出了加權(quán)優(yōu)化的辦法來(lái)提高診斷的可靠性。 最后,在實(shí)驗(yàn)室中模擬變轉(zhuǎn)速情況下的振動(dòng)波形,利用本文提出的特征參數(shù)指標(biāo)距的方法進(jìn)行診斷分析。得到了較好的結(jié)果,證明該方法在變轉(zhuǎn)速下對(duì)故障診斷的有效性。
[Abstract]:Absrtact: the technology of equipment condition monitoring and fault diagnosis is a kind of understanding and mastering the state of equipment in the process of using. The technique of monitoring the abnormal state of the equipment operation and judging the type of fault. The author found that the equipment was used as a mine equipment while serving the fault diagnosis project of Zhungel opencast mine shovel equipment of Shenhua Group. On behalf of the mine shovel, Its working conditions are abominable, the working conditions are complex and varied, and so on. The rolling bearing bears the greatest pressure, and works in the state of variable speed for a long time, which is the most vulnerable to damage. It is necessary to study the bearing fault diagnosis technology which can adapt to variable speed condition. The main contents and work of this paper are as follows:. Firstly, the failure form of rolling bearing and its fault diagnosis method are analyzed. By analyzing the sensitivity of time domain characteristic parameters to fault information, the kurtosis index is extracted as the effective characteristic parameters of fault diagnosis in this paper. Secondly, the vibration signal under the change of bearing speed is analyzed. Because the kurtosis index can not be used to characterize the complexity of vibration waveform, the approximate entropy and power spectrum entropy are added as a supplement. Furthermore, a multi-parameter fault feature extraction method for rolling bearing is proposed. By calculating the distance between the waveform to be detected and the characteristic parameter index, the fault type and the fault degree can be determined. This method can not only accurately distinguish the fault type, but also the fault type. Moreover, it can avoid the influence of rotating speed on fault diagnosis result, and it is proved to be feasible and effective by experiment. Thirdly, the diagnostic effects of 3D and 6-D feature distance are compared and analyzed respectively. The influence of increasing dimension on fault diagnosis effect is analyzed theoretically and verified by experiments. A weighted optimization method is proposed to improve the reliability of fault diagnosis. Finally, the vibration waveform under the condition of variable rotational speed is simulated in the laboratory, and the method proposed in this paper is used for diagnosis and analysis. Good results are obtained, and the effectiveness of this method for fault diagnosis under variable rotational speed is proved.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類(lèi)號(hào)】：TH133.33;TH165.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 殷金泉;樁基聲波透射法檢測(cè)信號(hào)處理研究[D];南昌航空大學(xué);2012年

2 吳利春;基于遺傳神經(jīng)網(wǎng)絡(luò)的故障診斷算法研究[D];遼寧大學(xué);2012年

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本文編號(hào)：1650971