【摘要】：齒輪系統(tǒng)在機(jī)械工程及其他很多領(lǐng)域的應(yīng)用范圍都十分廣泛。齒輪系統(tǒng)中所包含的零部件在發(fā)生故障時(shí)，在齒輪系統(tǒng)中會(huì)有周期性的脈沖沖擊力產(chǎn)生，，表現(xiàn)在頻譜上即為出現(xiàn)相應(yīng)的故障信號(hào)頻譜特征，例如齒輪發(fā)生磨損、點(diǎn)蝕，軸發(fā)生輕度彎曲或者裂紋等故障時(shí)都會(huì)在頻譜圖上產(chǎn)生相應(yīng)的特征信號(hào)。如果能在齒輪系統(tǒng)的故障早期從微弱的特征信號(hào)中提取故障調(diào)制信息，分析其強(qiáng)度和頻次從而判斷出零部件損傷的程度和部位，就可以較好的達(dá)到機(jī)械系統(tǒng)早期故障診斷的目的。 隨機(jī)共振是一種將混合信號(hào)輸入非線性系統(tǒng)后，通過(guò)非線性系統(tǒng)使噪聲的部分能量轉(zhuǎn)化給信號(hào)的方法，而以往的一些信號(hào)檢測(cè)方法均為通過(guò)抑制噪聲來(lái)達(dá)到檢測(cè)信號(hào)的目的，很明顯，在抑制噪聲的同時(shí)也必然導(dǎo)致信號(hào)本身的能量受到抑制和影響，所以隨機(jī)共振方法與傳統(tǒng)的信號(hào)檢測(cè)方法相比，其優(yōu)點(diǎn)就在于，當(dāng)噪聲中信號(hào)能量比較微弱時(shí)，它在抑制噪聲的同時(shí)反而能夠增加微弱信號(hào)的能量，從而達(dá)到更好的檢測(cè)微弱信號(hào)的目的。針對(duì)齒輪系統(tǒng)故障的振動(dòng)機(jī)理和振動(dòng)特點(diǎn)，本文中利用隨機(jī)共振方法對(duì)強(qiáng)噪聲中的齒輪系統(tǒng)故障振動(dòng)信號(hào)的微弱故障特征量進(jìn)行提取，以達(dá)到對(duì)齒輪系統(tǒng)的早期故障進(jìn)行更好的識(shí)別和診斷的目的，論文主要進(jìn)行了以下幾方面的工作： 本文首先基于非線性系統(tǒng)和隨機(jī)共振理論模型，通過(guò)建模和仿真實(shí)驗(yàn)，研究了隨機(jī)共振系統(tǒng)自身參數(shù)對(duì)于隨機(jī)共振系統(tǒng)輸出的影響規(guī)律。并對(duì)目前的幾種隨機(jī)共振大參數(shù)信號(hào)轉(zhuǎn)換方法的性能和適用范圍通過(guò)相應(yīng)的仿真實(shí)驗(yàn)進(jìn)行了分析與研究。 然后結(jié)合齒輪系統(tǒng)的實(shí)際故障振動(dòng)信號(hào)，研究了齒輪系統(tǒng)早期故障的振動(dòng)機(jī)理及常見(jiàn)的故障信號(hào)特征，包括齒輪、軸系故障產(chǎn)生的機(jī)理和不同故障的頻譜特征，得出了齒輪系統(tǒng)典型故障信號(hào)特征細(xì)化表。 在對(duì)齒輪系統(tǒng)故障的微弱振動(dòng)信號(hào)特征研究的基礎(chǔ)上，設(shè)計(jì)了對(duì)于齒輪系統(tǒng)早期故障更有針對(duì)性的自適應(yīng)隨機(jī)共振算法，并通過(guò)對(duì)仿真及實(shí)驗(yàn)的結(jié)果分析對(duì)算法做了相應(yīng)的改進(jìn)。在MFS機(jī)械綜合故障模擬實(shí)驗(yàn)臺(tái)上進(jìn)行了齒輪的磨損、點(diǎn)蝕，軸的裂紋和輕度彎曲等幾種齒輪系統(tǒng)典型早期故障的實(shí)驗(yàn)驗(yàn)證，結(jié)果表明，本文設(shè)計(jì)的自適應(yīng)隨機(jī)共振算法可以對(duì)齒輪系統(tǒng)中的故障微弱信號(hào)進(jìn)行較好的特征提取，即能夠較好的對(duì)齒輪系統(tǒng)進(jìn)行早期的故障識(shí)別。
[Abstract]:Gear system is widely used in mechanical engineering and many other fields. The components contained in the gear system will produce periodic impulse impact force in the gear system in the event of failure, which is shown in the spectrum of the corresponding fault signal spectrum characteristics, such as gear wear, pitting, and so on. When the shaft is slightly bent or cracked, the corresponding characteristic signals will be generated on the spectrum map. If the fault modulation information can be extracted from the weak characteristic signal in the early stage of the gear system fault, the intensity and frequency of the fault modulation information can be analyzed to determine the extent and position of the damage of the parts. It can achieve the purpose of early fault diagnosis of mechanical system. Stochastic resonance (SR) is a method by which the mixed signal is input into a nonlinear system and the partial energy of the noise is converted to the signal by the nonlinear system. However, some of the signal detection methods in the past are designed to suppress the noise to achieve the purpose of detecting the signal. Obviously, when the noise is suppressed, the energy of the signal itself must be suppressed and affected. So compared with the traditional signal detection method, the advantage of the stochastic resonance method is that when the signal energy in the noise is relatively weak, the random resonance method has the advantage over the traditional signal detection method. It can not only restrain the noise but also increase the energy of the weak signal, so that it can detect the weak signal better. In view of the vibration mechanism and vibration characteristics of gear system fault, the weak fault characteristic of gear system fault vibration signal in strong noise is extracted by stochastic resonance method in this paper. In order to better identify and diagnose the early faults of gear system, the main work of this paper is as follows: firstly, based on nonlinear system and stochastic resonance theory model, modeling and simulation experiments are carried out. The influence of the parameters of the stochastic resonance system on the output of the stochastic resonance system is studied. The performance and application range of several random resonance large parameter signal conversion methods are analyzed and studied through the corresponding simulation experiments. Then combined with the actual vibration signals of the gear system, the vibration mechanism and the common fault signal characteristics of the gear system early fault are studied, including the mechanism of the gear and shaft system fault generation and the spectrum characteristics of different faults. The characteristic refinement table of typical fault signal of gear system is obtained. Based on the study of the weak vibration signal characteristics of gear system fault, an adaptive stochastic resonance algorithm is designed, which is more specific to the early fault of gear system. The algorithm is improved by analyzing the simulation and experiment results. Several typical early failures of gear system, such as wear, pitting, shaft crack and slight bending, were tested on the MFS mechanical comprehensive fault simulator. The results show that: The adaptive stochastic resonance algorithm designed in this paper can extract the weak signal of the gear system better, that is, the early fault identification of the gear system can be better.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH132.41;TH165.3

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