本文選題：滾動(dòng)軸承 + 故障狀態(tài)識(shí)別�。� 參考：《沈陽(yáng)理工大學(xué)》2017年碩士論文

【摘要】：作為旋轉(zhuǎn)機(jī)械中重要零件的滾動(dòng)軸承,在現(xiàn)今的各個(gè)行業(yè)有著非常廣泛的應(yīng)用,但由于經(jīng)常在較為復(fù)雜的環(huán)境下工作,滾動(dòng)軸承也是各類機(jī)械中最易損壞的零件之一,滾動(dòng)軸承的損壞往往會(huì)導(dǎo)致整個(gè)機(jī)械設(shè)備的故障,引發(fā)更為嚴(yán)重的后果,而滾動(dòng)軸承的壽命通常又具有離散性。因此,對(duì)滾動(dòng)軸承的狀態(tài)以及故障類型進(jìn)行準(zhǔn)確的識(shí)別就顯得尤為重要了。本論文采用一種改進(jìn)的集合經(jīng)驗(yàn)?zāi)B(tài)分解(Ensemble Empirical Mode Decomposition,EEMD)對(duì)滾動(dòng)軸承的振動(dòng)信號(hào)進(jìn)行降噪和分解,得到若干個(gè)固有模態(tài)函數(shù)(Intrinsic mode function,IMF),再計(jì)算包含主要故障信息的IMF分量的能量分布作為滾動(dòng)軸承的故障特征。該方法根據(jù)在分解過(guò)程中的信號(hào)和所加入的白噪聲的特點(diǎn)來(lái)選擇各項(xiàng)參數(shù),這樣就避免了傳統(tǒng)EEMD方法根據(jù)經(jīng)驗(yàn)人為主觀的設(shè)置參數(shù)導(dǎo)致分解結(jié)果不夠準(zhǔn)確的問(wèn)題。由于傳統(tǒng)的自組織映射神經(jīng)網(wǎng)絡(luò)(Self-organizing Feature Map,SOM)網(wǎng)絡(luò)在滾動(dòng)軸承的故障狀態(tài)識(shí)別的過(guò)程中存在訓(xùn)練時(shí)間較長(zhǎng),識(shí)別準(zhǔn)確率有待提高等不足。因此,本論文采用免疫遺傳算法(Immune Genetic Algorithm , IGA)對(duì)SOM的權(quán)值更新過(guò)程進(jìn)行優(yōu)化。IGA通過(guò)模仿生物的免疫機(jī)制以及遺傳的規(guī)則來(lái)優(yōu)化SOM的權(quán)值更新過(guò)程,使權(quán)值的更新更為快速且全面,這樣也就能夠進(jìn)一步提高SOM網(wǎng)絡(luò)對(duì)滾動(dòng)軸故障類型識(shí)別的速度以及準(zhǔn)確性。最后將提取到的滾動(dòng)軸承的故障特征輸入到經(jīng)過(guò)免疫遺傳算法優(yōu)化后的自組織映射(IGA-SOM)網(wǎng)絡(luò)當(dāng)中,通過(guò)IGA-SOM對(duì)滾動(dòng)軸承的故障類型進(jìn)行識(shí)別。通過(guò)實(shí)際的滾動(dòng)軸承的故障數(shù)據(jù)驗(yàn)證表明,本論文的方法可以實(shí)現(xiàn)對(duì)于滾動(dòng)軸承故障類型的高效且準(zhǔn)確地識(shí)別。
[Abstract]:As an important part of rotating machinery, rolling bearings are widely used in various industries nowadays. However, because they often work in complex environments, rolling bearings are also one of the most easily damaged parts in all kinds of machinery. The failure of rolling bearings often leads to the failure of the whole mechanical equipment, which leads to more serious consequences, and the life of rolling bearings is usually discrete. Therefore, it is very important to identify the status and fault types of rolling bearings. In this paper, an improved set empirical mode decomposition (EMD) is used to reduce the noise and decompose the vibration signals of rolling bearings. Some intrinsic mode functions are obtained, and the energy distribution of the components containing the main fault information is calculated as the fault characteristics of the rolling bearing. This method selects the parameters according to the characteristics of the signal and the white noise added in the decomposition process, thus avoiding the problem that the traditional EEMD method sets the parameters artificially and empirically, which results in inaccurate decomposition results. Because the traditional self-organizing mapping neural network self-organizing feature Map-SOM network has a long training time in the process of rolling bearing fault state recognition, the recognition accuracy needs to be improved. Therefore, in this paper, the immune genetic algorithm (IGA) is used to optimize the weight updating process of SOM. IGA optimizes the weight updating process by imitating the immune mechanism of organisms and genetic rules, so that the weight updating process is faster and more comprehensive. This will further improve the speed and accuracy of SOM network in identifying rolling shaft fault types. Finally, the extracted fault features of rolling bearings are input into the self-organizing mapping IGA-SOM network optimized by immune genetic algorithm, and the fault types of rolling bearings are identified by IGA-SOM. The actual fault data verification of rolling bearing shows that the method of this paper can be used to identify the fault type of rolling bearing efficiently and accurately.
【學(xué)位授予單位】：沈陽(yáng)理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH133.33;TP18

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本文編號(hào)：2023890