本文選題：故障診斷　切入點(diǎn)：變轉(zhuǎn)速滾動(dòng)軸承　出處：《北京交通大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)在,機(jī)械設(shè)備工作環(huán)境變化多樣,機(jī)械狀態(tài)的變化導(dǎo)致振動(dòng)信號(hào)存在有大量的非平穩(wěn)信號(hào)。長(zhǎng)久以來(lái),大部分機(jī)械故障診斷都是根據(jù)平穩(wěn)工況下所體現(xiàn)的信息情況來(lái)分析的。然而,隨著故障診斷的快速發(fā)展和現(xiàn)實(shí)要求的不斷提高,如何對(duì)非平穩(wěn)信號(hào)進(jìn)行有效分析成為故障診斷領(lǐng)域的一個(gè)關(guān)鍵問(wèn)題。 本文采用了EMD重采樣方法對(duì)變轉(zhuǎn)速滾動(dòng)軸承振動(dòng)信號(hào)的EMD分解結(jié)果進(jìn)行了平穩(wěn)化處理,有效的消減了轉(zhuǎn)速變化對(duì)信號(hào)頻域分析的影響,并根據(jù)粗糙集方法分析重采樣信號(hào)特征,判別故障,取得了良好的效果。 首先,分析、實(shí)現(xiàn)并驗(yàn)證了EMD分解和EMD重采樣方法,利用該方法對(duì)變頻模擬信號(hào)的分析結(jié)果表明重采樣方法可以令I(lǐng)MF頻域特征更加明顯,說(shuō)明該方法對(duì)分析變轉(zhuǎn)速信號(hào)是有效的。 其次,根據(jù)特征參數(shù)分析方法,計(jì)算了傳統(tǒng)時(shí)域、頻域特征參數(shù)對(duì)EMD重采樣結(jié)果特征敏感性,篩選了用以表征狀態(tài)特征的特征參數(shù)與IMF,得到了表征信號(hào)特征明顯的特征參數(shù)。 最后,將所得的信號(hào)特征值作為其條件屬性,根據(jù)粗糙集方法對(duì)特征屬性進(jìn)行了屬性值約簡(jiǎn),得出了用于診斷故障的決策表。設(shè)計(jì)了變轉(zhuǎn)速滾動(dòng)軸承故障診斷方法流程,并通過(guò)實(shí)驗(yàn)證明該方法有效,診斷準(zhǔn)確度比較高。
[Abstract]:Now, a variety of mechanical equipment working environment, changes in mechanical condition lead to vibration signal has a large number of non-stationary signal. For a long time, most of the machinery fault diagnosis are information based on the stationary condition of analysis. However, with the rapid development of fault diagnosis and the practical requirements continue to increase, how to effectively the analysis of non-stationary signals is becoming a key issue in the field of fault diagnosis.
This paper uses the variable speed of rolling bearing vibration signal EMD decomposition results of the stationary processing EMD resampling method, effectively reduce the impact analysis of the speed change of signal frequency, and the fault analysis method based on rough set resampling signals, and achieved good results.
First, we analyze, implement and verify the EMD decomposition and EMD resampling method. Using the method to analyze the frequency conversion analog signal, it is shown that the resampling method can make the IMF frequency domain characteristics more obvious, which shows that this method is effective for analyzing the variable speed signal.
Secondly, according to the characteristic parameter analysis method, we calculated the sensitivity of the traditional time domain and frequency domain characteristic parameters to the EMD resampling results, and screened the characteristic parameters and IMF used to characterize the state characteristics, and got the characteristic parameters that characterize the signal characteristics.
Finally, the signal characteristics of the value as its condition attributes, based on rough set method of feature attributes by attribute value reduction, decision table for fault diagnosis is obtained. The design process of variable speed rolling bearing fault diagnosis method, and the experimental results proved that the method is effective, the diagnostic accuracy is relatively high.

【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】：2012
【分類號(hào)】：TH133.331;TH165.3

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