本文選題：混沌弱信號(hào)檢測(cè)技術(shù) + 微弱信號(hào)　； 參考：《內(nèi)蒙古科技大學(xué)》2013年碩士論文

【摘要】：滾動(dòng)軸承是現(xiàn)如今應(yīng)用最多的機(jī)械部件之一，現(xiàn)場(chǎng)大型設(shè)備基本都是旋轉(zhuǎn)機(jī)械，幾乎每臺(tái)旋轉(zhuǎn)機(jī)械上都能看到軸承的身影。每年因?yàn)樾D(zhuǎn)機(jī)械故障而造成的經(jīng)濟(jì)損失高達(dá)數(shù)百億，而因?yàn)檩S承問題導(dǎo)致的故障幾乎占到所有旋轉(zhuǎn)機(jī)械故障的1/3以上。任何故障都是從輕微開始，直到造成嚴(yán)重事故。因此在軸承出現(xiàn)故障之初就將其檢測(cè)出來非常重要。 作為非線性科學(xué)的分支——混沌理論，現(xiàn)如今已經(jīng)在很多工程領(lǐng)域都得到了廣泛應(yīng)用。把混沌理論應(yīng)用到微弱信號(hào)檢測(cè)中去是目前研究的軸承故障問題的主要方法之一。本文基于國(guó)內(nèi)外混沌理論的研究基礎(chǔ)上，廣泛吸取了各領(lǐng)域?qū)煦缋碚摰纳羁汤斫夂脱芯砍晒�，利用混沌理論來識(shí)別滾動(dòng)軸承微弱故障信號(hào)中所包含的軸承故障信息，判斷出軸承故障位置。 本文主要做了如下幾個(gè)方面的內(nèi)容： 首先，本文介紹了混沌的基本概念、發(fā)展歷程、主要成就，以及常見故障診斷的判別方法，敘述了混沌理論對(duì)現(xiàn)代科學(xué)的主要影響和意義。并且對(duì)弱信號(hào)進(jìn)行了簡(jiǎn)單的介紹，給出了簡(jiǎn)單的處理方法，并且對(duì)信號(hào)的性能進(jìn)行了綜述。 其次，建立了檢測(cè)混沌微弱周期信號(hào)的模型。利用Duffing混沌系統(tǒng)的動(dòng)力學(xué)行為特點(diǎn)來檢測(cè)微弱周期信號(hào)，向處在臨界周期狀態(tài)的系統(tǒng)中加入微弱周期信號(hào)，觀察Duffing方程的相軌跡圖是否發(fā)生突變。從而可以反映出是否存在信噪比較大的微弱周期信號(hào)。 最后，對(duì)傳統(tǒng)Duffing方程做了進(jìn)一步的改進(jìn)，并利用改進(jìn)方程來檢測(cè)軸承點(diǎn)蝕故障微弱振動(dòng)信號(hào)。軸承故障信號(hào)是多種不同信號(hào)混合在一起所形成的非常復(fù)雜的復(fù)合信號(hào)。軸承故障信號(hào)具有一定的混沌特性。每一種不同型號(hào)的軸承或者是同一軸承不同位置的特征信號(hào)都不相同。根據(jù)軸承的這一特性提出了基于混沌理論的檢測(cè)軸承故障的方法。該方法只對(duì)特征信號(hào)敏感，而對(duì)噪聲信號(hào)具有很好的抑制作用。對(duì)于初期采集到的信號(hào)由于存在許多無用信號(hào)，為了減小計(jì)算量，，需要在利用Duffing方程進(jìn)行分析之前，事先利用小波包降噪將原始信號(hào)進(jìn)行初步降噪。 通過最終的結(jié)果表明利用小波理論和混沌理論能夠很好的將滾動(dòng)軸承外圈早期故障信息反映出來，從而說明該方法是判斷滾動(dòng)軸承故障問題的有效方法。
[Abstract]:Rolling bearing is one of the most widely used mechanical parts nowadays. The field large-scale equipment is basically rotating machinery, almost every rotating machine can see the shape of bearing. Every year, the economic loss caused by rotating machinery faults is as high as tens of billions, and the faults caused by bearing problems account for almost a third of all rotating machinery failures. Any malfunction starts slightly until it causes a serious accident. Therefore, it is very important to detect the bearing at the beginning of the failure. As a branch of nonlinear science, chaos theory has been widely used in many engineering fields. Applying chaos theory to weak signal detection is one of the main methods of bearing fault problem. Based on the research of chaos theory at home and abroad, this paper has widely absorbed the deep understanding and research results of chaos theory in various fields, and used chaos theory to identify the bearing fault information contained in the weak fault signal of rolling bearing. Determine the bearing fault location. The main contents of this paper are as follows: First of all, this paper introduces the basic concept of chaos, the development of chaos, the main achievements, as well as the common fault diagnosis method, and describes the main impact and significance of chaos theory on modern science. The weak signal is introduced briefly, the processing method is given, and the performance of the signal is summarized. Secondly, a model for detecting chaotic weak periodic signals is established. The weak periodic signal is detected by the dynamic behavior of the Duffing chaotic system, and the weak periodic signal is added to the system in the critical periodic state. The phase locus of the Duffing equation is observed to be abrupt. Therefore, the existence of weak periodic signal with high SNR can be reflected. Finally, the traditional Duffing equation is further improved, and the improved equation is used to detect the weak vibration signal of bearing pitting fault. Bearing fault signal is a very complex composite signal formed by the mixing of many different signals. The bearing fault signal has certain chaos characteristic. Each type of bearing or the same bearing at different locations has different characteristic signals. According to this characteristic of bearing, a method of detecting bearing fault based on chaos theory is proposed. This method is only sensitive to the characteristic signal and has a good suppression effect on the noise signal. Because there are many useless signals in the initial signal, in order to reduce the computational complexity, it is necessary to reduce the initial noise of the original signal by using wavelet packet denoising before using the Duffing equation to analyze the signal. The final results show that the wavelet theory and chaos theory can well reflect the early fault information of the outer ring of rolling bearing, which shows that this method is an effective method to judge the fault problem of rolling bearing.
【學(xué)位授予單位】：內(nèi)蒙古科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TH133.33

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