【摘要】：在現(xiàn)代化生產(chǎn)中，重大關(guān)鍵設(shè)備的實(shí)時(shí)監(jiān)控和故障診斷具有重要意義。然而，在很多設(shè)備的早期故障中，故障信號(hào)往往被噪聲所淹沒而難以被檢測(cè)出來，所以微弱信號(hào)的檢測(cè)顯得尤為重要，受到越來越多的重視。 首先課題以二階線性系統(tǒng)為信號(hào)檢測(cè)處理模型，提出基于二階線性動(dòng)力系統(tǒng)參數(shù)調(diào)節(jié)共振的微弱信號(hào)處理方法。其基本原理是：把包含有特征頻率成分和噪聲干擾的待檢測(cè)信號(hào)當(dāng)作系統(tǒng)的激勵(lì)，而把線性動(dòng)力系統(tǒng)看作信號(hào)處理模型。通過人為調(diào)節(jié)系統(tǒng)的固有頻率和阻尼比等參數(shù)，，使固有頻率與待檢測(cè)信號(hào)中的特征頻率相等，于是系統(tǒng)響應(yīng)達(dá)到共振，進(jìn)而使特征信號(hào)進(jìn)一步突出而克服噪聲，達(dá)到檢測(cè)特征信號(hào)成分的目的。該方法的具體實(shí)施是，在得到系統(tǒng)響應(yīng)最大值隨固有頻率變化的特性曲線后，根據(jù)曲線中的極大值即可識(shí)別噪聲中的特征信號(hào)。這種基于線性系統(tǒng)模型的信號(hào)檢測(cè)，可通過選取合適的系統(tǒng)阻尼比參數(shù)來優(yōu)化檢測(cè)結(jié)果。 其次課題以典型的Duffing系統(tǒng)為對(duì)象，研究了基于二階非線性系統(tǒng)隨機(jī)共振的信息檢測(cè)方法。該方法通過非線性雙穩(wěn)系統(tǒng)、待測(cè)微弱信號(hào)和信號(hào)中疊加的噪聲三者達(dá)到最佳匹配，使噪聲能量向信號(hào)轉(zhuǎn)移，達(dá)到微弱信號(hào)能量的加強(qiáng)，從而實(shí)現(xiàn)微弱故障信號(hào)的提取。對(duì)于滿足絕熱近似理論的小參數(shù)信號(hào)來說，可以直接應(yīng)用雙穩(wěn)系統(tǒng)隨機(jī)共振進(jìn)行信號(hào)檢測(cè)。而對(duì)于大參數(shù)信號(hào)而言，應(yīng)用變尺度隨機(jī)共振方法可通過對(duì)大頻率信號(hào)的頻率進(jìn)行線性壓縮，使信號(hào)轉(zhuǎn)化為滿足絕熱近似理論的小參數(shù)信號(hào)，從而實(shí)現(xiàn)大參數(shù)信號(hào)的隨機(jī)共振檢測(cè)。Duffing系統(tǒng)本身參數(shù)的調(diào)節(jié)可使系統(tǒng)和信號(hào)能夠與大噪聲達(dá)到最佳匹配，提高隨機(jī)共振檢測(cè)效果。 最后課題將基于二階線性系統(tǒng)與非線性Duffing系統(tǒng)的信號(hào)檢測(cè)方法分別應(yīng)用于轉(zhuǎn)子系統(tǒng)故障診斷中，對(duì)兩種方法進(jìn)行了比較分析，認(rèn)為對(duì)于采樣點(diǎn)數(shù)少、噪聲較低，且存在大幅值頻率干擾的故障信號(hào)，基于線性系統(tǒng)的檢測(cè)方法要優(yōu)于非線性Duffing系統(tǒng)的隨機(jī)共振檢測(cè)。
[Abstract]:In modern production, real-time monitoring and fault diagnosis of important key equipment is of great significance. However, in the early fault of many equipment, the fault signal is often submerged by noise and difficult to detect, so the detection of weak signal is especially important, and more attention has been paid to it. Based on the second-order linear system as the signal detection processing model, a weak signal processing method based on the parameter-adjusted resonance of the second-order linear dynamic system is proposed. The basic principle is that the signal to be detected including the characteristic frequency component and noise interference is regarded as the excitation of the system, while the linear dynamic system is regarded as the signal processing model. By artificially adjusting the natural frequency and damping ratio of the system, the natural frequency is equal to the characteristic frequency of the signal to be detected, so that the response of the system reaches resonance, and then the characteristic signal is further prominent and the noise is overcome. To achieve the purpose of detecting the components of the characteristic signal. The concrete implementation of this method is that the characteristic signal in the noise can be identified according to the maximum value in the curve after the characteristic curve of the maximum value of the system response varying with the natural frequency is obtained. This kind of signal detection based on linear system model can optimize the detection result by selecting the appropriate damping ratio parameters of the system. Secondly, taking the typical Duffing system as the object, the information detection method based on stochastic resonance of second-order nonlinear system is studied. By means of the nonlinear bistable system, the weak signal to be measured and the noise superimposed in the signal are matched optimally, so that the noise energy is transferred to the signal and the weak signal energy is enhanced, thus the weak fault signal is extracted. For the small parameter signal which satisfies the adiabatic approximation theory, the bistable system stochastic resonance can be directly used to detect the signal. For large-parameter signals, the variable-scale stochastic resonance method can be used to transform the large-frequency signals into small-parameter signals which satisfy the adiabatic approximation theory by linearly compressing the frequency of large-frequency signals. So the random resonance detection of large parameter signal can be realized. The adjustment of duffing system parameters can make the system and signal best match with large noise, and improve the effect of random resonance detection. Finally, the signal detection method based on the second-order linear system and the nonlinear Duffing system is applied to the fault diagnosis of the rotor system respectively. The two methods are compared and analyzed, and the results show that the sampling points are less and the noise is lower for the two methods. The detection method based on linear system is better than the stochastic resonance detection of nonlinear Duffing system for the fault signal with large amplitude and frequency interference.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

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