發(fā)布時(shí)間：2018-01-05 20:07

  本文關(guān)鍵詞：滾動(dòng)軸承故障非接觸多傳感器聲信號(hào)融合及診斷技術(shù)研究　出處：《東北石油大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 聲發(fā)射 滾動(dòng)軸承 故障診斷 非接觸 多傳感器融合

【摘要】：滾動(dòng)軸承是各工業(yè)領(lǐng)域的基本元件，在各類(lèi)機(jī)械設(shè)備中扮演著重要的角色。滾動(dòng)軸承的故障診斷方法有很多種，如紅外軸溫探測(cè)法、振動(dòng)信號(hào)分析法、潤(rùn)滑油液分析法等，這些方法各具特色，都能夠?qū)ΤＲ?jiàn)的滾動(dòng)軸承故障類(lèi)型做出有效判斷。但這些方法由于自身特點(diǎn)及使用環(huán)境的限制，都不能有效地診斷滾動(dòng)軸承早期故障。另外，在役滾動(dòng)軸承往往處于移動(dòng)狀態(tài)，移動(dòng)中滾動(dòng)軸承運(yùn)行狀態(tài)的監(jiān)測(cè)是各工業(yè)領(lǐng)域的技術(shù)難題。為了保證機(jī)械設(shè)備安全、平穩(wěn)的運(yùn)行，降低事故的發(fā)生率，本文研究了基于周期性非接觸聲發(fā)射信號(hào)的滾動(dòng)軸承故障診斷方法，目的在于完善滾動(dòng)軸承故障診斷技術(shù)，為滾動(dòng)軸承狀態(tài)監(jiān)測(cè)奠定基礎(chǔ)。 搭建了滾動(dòng)軸承故障非接觸多傳感器聲發(fā)射檢測(cè)試驗(yàn)臺(tái)，對(duì)移動(dòng)中帶有滾動(dòng)體、內(nèi)圈、外圈故障的滾動(dòng)軸承，分別進(jìn)行了不同轉(zhuǎn)速、不同移動(dòng)速度的故障聲發(fā)射信號(hào)采集。研究了形態(tài)學(xué)濾波技術(shù)，根據(jù)滾動(dòng)軸承各類(lèi)故障聲發(fā)射信號(hào)的特點(diǎn)，找到了合適的結(jié)構(gòu)元素并對(duì)各組試驗(yàn)數(shù)據(jù)進(jìn)行了形態(tài)學(xué)濾波處理，剔除了低頻、高頻噪聲等干擾信息，為后續(xù)數(shù)據(jù)分析掃清了障礙。 針對(duì)多傳感器采集時(shí)信號(hào)重疊和不完整問(wèn)題，根據(jù)聲發(fā)射撞擊信號(hào)時(shí)差和傳感器陣列幾何關(guān)系建立同聲源信號(hào)判別公式，以及多傳感器同聲源信號(hào)融合算法，對(duì)各組試驗(yàn)信號(hào)進(jìn)行了辨識(shí)融合處理，處理結(jié)果表明融合信號(hào)與故障源信號(hào)的相似程度高于各同聲源信號(hào)。 提出了基于周期性聲發(fā)射撞擊計(jì)數(shù)的滾動(dòng)軸承故障診斷方法和基于周期性聲信號(hào)特征參數(shù)的滾動(dòng)軸承故障診斷方法。前者利用滾動(dòng)軸承故障融合信號(hào)的數(shù)量與聲發(fā)射累計(jì)撞擊計(jì)數(shù)的對(duì)應(yīng)關(guān)系診斷滾動(dòng)軸承故障；后者通過(guò)計(jì)算滾動(dòng)軸承故障周期性聲信號(hào)的波形特征參數(shù)診斷滾動(dòng)軸承故障。診斷結(jié)果表明前者在小周期測(cè)試時(shí)精確度較高，多周期測(cè)試時(shí)存在一定的誤差，但在可承受范圍以?xún)?nèi)；后者由于是對(duì)周期性聲信號(hào)進(jìn)行處理，排除了個(gè)別非故障源信號(hào)的干擾，因而具有較高的準(zhǔn)確率。兩種方法均可用于滾動(dòng)軸承早期故障判斷，并有效區(qū)分故障類(lèi)型。
[Abstract]:The rolling bearing is the basic element of various industries, plays an important role in all kinds of mechanical equipment. There are many kinds of fault diagnosis method of rolling bearing, such as infrared axle temperature detection method, vibration signal analysis method, the lubricating oil analysis method etc. these methods have their own characteristics, are able to make effective judgment on rolling bearing fault common types. But these methods because of its own characteristics and the use of environmental constraints, can effectively diagnose the incipient fault of rolling bearing. In addition, in the service of rolling bearings are often in a mobile state, monitoring the running state of rolling bearing movement is a technical problem in various industrial fields. In order to ensure the safety of machinery and equipment running smoothly. Reduce the incidence of accidents, this paper studied the periodic non contact fault diagnosis method of rolling bearing based on acoustic emission signal, in order to improve the fault diagnosis of rolling bearing off technology for The foundation of rolling bearing state monitoring is laid.
Build a rolling bearing fault of non contacting sensor acoustic emission testing bench, to move with the rolling body, the inner ring, the outer ring of the rolling bearing fault, respectively, different speed, different moving speed of fault acoustic emission signal acquisition. Research on the morphological filtering technology, according to the characteristics of rolling bearing faults of AE signal, find the appropriate structural elements and each group of test data were studied by morphological filtering, eliminating low frequency, high frequency noise and other interference information for subsequent data analysis to clear the obstacle.
According to the multi sensor signal overlap and incomplete problems, according to the acoustic emission signal and time impact sensor array geometry simultaneous source signal discrimination formula is set up, and the multi-sensor fusion algorithm for simultaneous signals, each test signal identification of fusion processing results show that the degree of similarity fusion signal and fault source signal is higher than the simultaneous the source of the signal.
The cumulative impact count of acoustic emission of the rolling bearing fault diagnosis method and fault diagnosis method of rolling bearing cyclic acoustic signals based on characteristic parameters. Based on the former by the number of fusion signals and the acoustic emissioncumulative impact rolling bearing fault count the corresponding relationship between the fault diagnosis of rolling bearing; the latter through the calculation of rolling bearing fault diagnosis waveform characteristic parameters of bearing periodic fault acoustic signal of rolling. The diagnosis results show that the former higher accuracy in a few period test, there are some errors in multi period test, but within the acceptable range; because the latter is the processing of periodic acoustic signal, eliminate the interference of individual non fault source signals, so it has high accuracy. The two methods can be used for rolling bearing early faults judgment, and distinguish fault types.

【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TH133.33;TH165.3

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