基于隨機共振的齒輪系統(tǒng)故障微弱信號提取技術研究
發(fā)布時間：2019-03-12 19:36
【摘要】：齒輪系統(tǒng)在機械工程及其他很多領域的應用范圍都十分廣泛。齒輪系統(tǒng)中所包含的零部件在發(fā)生故障時，在齒輪系統(tǒng)中會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)相應的故障信號頻譜特征，例如齒輪發(fā)生磨損、點蝕，軸發(fā)生輕度彎曲或者裂紋等故障時都會在頻譜圖上產(chǎn)生相應的特征信號。如果能在齒輪系統(tǒng)的故障早期從微弱的特征信號中提取故障調制信息，分析其強度和頻次從而判斷出零部件損傷的程度和部位，就可以較好的達到機械系統(tǒng)早期故障診斷的目的。 隨機共振是一種將混合信號輸入非線性系統(tǒng)后，通過非線性系統(tǒng)使噪聲的部分能量轉化給信號的方法，而以往的一些信號檢測方法均為通過抑制噪聲來達到檢測信號的目的，很明顯，在抑制噪聲的同時也必然導致信號本身的能量受到抑制和影響，所以隨機共振方法與傳統(tǒng)的信號檢測方法相比，其優(yōu)點就在于，當噪聲中信號能量比較微弱時，它在抑制噪聲的同時反而能夠增加微弱信號的能量，從而達到更好的檢測微弱信號的目的。針對齒輪系統(tǒng)故障的振動機理和振動特點，本文中利用隨機共振方法對強噪聲中的齒輪系統(tǒng)故障振動信號的微弱故障特征量進行提取，以達到對齒輪系統(tǒng)的早期故障進行更好的識別和診斷的目的，論文主要進行了以下幾方面的工作： 本文首先基于非線性系統(tǒng)和隨機共振理論模型，通過建模和仿真實驗，研究了隨機共振系統(tǒng)自身參數(shù)對于隨機共振系統(tǒng)輸出的影響規(guī)律。并對目前的幾種隨機共振大參數(shù)信號轉換方法的性能和適用范圍通過相應的仿真實驗進行了分析與研究。 然后結合齒輪系統(tǒng)的實際故障振動信號，研究了齒輪系統(tǒng)早期故障的振動機理及常見的故障信號特征，包括齒輪、軸系故障產(chǎn)生的機理和不同故障的頻譜特征，得出了齒輪系統(tǒng)典型故障信號特征細化表。 在對齒輪系統(tǒng)故障的微弱振動信號特征研究的基礎上，設計了對于齒輪系統(tǒng)早期故障更有針對性的自適應隨機共振算法，并通過對仿真及實驗的結果分析對算法做了相應的改進。在MFS機械綜合故障模擬實驗臺上進行了齒輪的磨損、點蝕，軸的裂紋和輕度彎曲等幾種齒輪系統(tǒng)典型早期故障的實驗驗證，結果表明，本文設計的自適應隨機共振算法可以對齒輪系統(tǒng)中的故障微弱信號進行較好的特征提取，即能夠較好的對齒輪系統(tǒng)進行早期的故障識別。
[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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH132.41;TH165.3
本文編號：2439088
[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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH132.41;TH165.3
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本文編號：2439088
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