本文關(guān)鍵詞：基于改進(jìn)二維互補(bǔ)隨機(jī)共振的微弱信號檢測方法及應(yīng)用研究　出處：《安徽大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 噪聲增強(qiáng) 軸承故障診斷 隨機(jī)共振 集總平均 加權(quán)功率譜峭度 微弱信號檢測 信噪比 參數(shù)調(diào)整

【摘要】：機(jī)械設(shè)備的故障診斷對于保障人民大眾的生命安全、減小不必要的經(jīng)濟(jì)損失以及避免社會生產(chǎn)的進(jìn)度停滯、自然環(huán)境的保護(hù)有著極為重要的實際意義。在工業(yè)現(xiàn)場,對機(jī)械設(shè)備采用一系列有效的手段檢測設(shè)備是否產(chǎn)生了故障或者是否有產(chǎn)生故障的趨勢,這一舉措很具有必要性。檢測機(jī)械是否有故障需要采集機(jī)械運(yùn)行狀態(tài)下的聲音、振動或者電流等信號,對信號處理分析。但是,從傳感器采集到的信號一般情況下都會含有大量來自其他機(jī)械零部件的運(yùn)行噪聲和環(huán)境噪聲,這些噪聲成分會使處理信號的過程變得很困難。所以,微弱信號故障診斷對于抑制噪聲,增強(qiáng)微弱信號,提高故障診斷的效果有很重要的意義。從信號處理的角度上來說,傳統(tǒng)的一維隨機(jī)共振方法(one-dimensional stochastic resonance,1DSR)是一種很特殊的非線性濾波器,它能夠利用一定量的噪聲增強(qiáng)放大非線性系統(tǒng)中的微弱周期信號,這種特殊的濾波去噪機(jī)制也是傳統(tǒng)線性濾波器所不具備的。因此,1DSR對于信號頻帶和噪聲頻帶重疊的微弱信號提取,擁有很廣泛的應(yīng)用前景。然而,1DSR由于本身性質(zhì)類似于一個低通濾波器,在濾波效果上還有待進(jìn)一步改進(jìn)。本文研究的是一種基于利用噪聲增強(qiáng)周期信號,被稱之為二維隨機(jī)共振的微弱信號檢測算法,并且還有該算法在軸承故障診斷方面的應(yīng)用。本文分析和討論了離散隨機(jī)共振的微弱信號提取算法,在這個基礎(chǔ)上,提出了一種新型的集總平均二維隨機(jī)共振(ensemble average two-dimensional stochastic resonance,E2DSR)算法,E2DSR方法是采用二維隨機(jī)共振和集總平均方法相結(jié)合在一起,構(gòu)建成了 E2DSR模型。E2DSR模型的輸出信號信噪比 (signal-noise ratio,SNR)也比1DSR的輸出信噪比要高,故障特征頻率在功率譜中能夠很明顯地凸顯出來。仿真試驗和軸承數(shù)據(jù)實驗結(jié)果都驗證了 E2DSR算法的優(yōu)點(diǎn),這種新型的算法具有比1DSR更高效的抑制噪聲的性能,具有卓越的噪聲消除和微弱信號檢測能力。為了進(jìn)一步研究二維隨機(jī)共振,本文又提出了一種自適應(yīng)的二維互補(bǔ)隨機(jī)共振(two-dimensional complementary stochastic resonance,2DCSR)方法,首先將傳感器采集到的軸承信號進(jìn)行帶通濾波和共振解調(diào),隨后將包絡(luò)信號對半分成兩段信作為2DCSR的輸入,利用輸出信號的加權(quán)功率譜峭度(weighted power spectrum kurtosis,WPSK)自適應(yīng)調(diào)節(jié)系統(tǒng)的參數(shù)以達(dá)到參數(shù)最優(yōu)化,接著利用二維隨機(jī)共振的兩個輸出,其中一個輸出信號增強(qiáng)另外一個輸出信號,最終得到最優(yōu)輸出結(jié)果及其功率譜,用來識別軸承故障類型。數(shù)值仿真和實驗結(jié)果,還有輸出信號的WPSK值對比都表明,2DCSR可以很有效地提高軸承故障診斷的效果。綜上所述,本文研究了基于改進(jìn)的二維互補(bǔ)隨機(jī)共振方法的噪聲增強(qiáng)微弱信號檢測和在軸承故障診斷方面的應(yīng)用。本文提出的兩種方法都和傳統(tǒng)的1DSR方法進(jìn)行了等條件下的對比,擁有抑制噪聲性強(qiáng)、濾波效果好、易于實現(xiàn)等突出的優(yōu)點(diǎn)。與此同時,實際故障信號也證明了改進(jìn)的二維互補(bǔ)隨機(jī)共振方法的優(yōu)越性和實用性。
[Abstract]:The failure diagnosis of mechanical equipment is very important for ensuring the life safety of the masses, reducing unnecessary economic losses, avoiding the stagnation of social production and protecting the natural environment. In the industrial field, a series of effective measures for machinery and equipment are used to detect whether the equipment has malfunction or whether there is a trend of failure. This measure is very necessary. It is necessary to collect sound, vibration or current signals in the state of mechanical operation to detect or analyze the signal processing. However, in general, signals collected from sensors will contain a lot of operational noise and environmental noise from other mechanical components. These noise components will make the process of signal processing very difficult. Therefore, the fault diagnosis of weak signal is of great significance to the suppression of noise, the enhancement of weak signal and the improvement of the effect of fault diagnosis. From the signal processing point of view, the traditional method of stochastic resonance (one-dimensional stochastic resonance, 1DSR) is a kind of special nonlinear filter, amplification of weak periodic signal in nonlinear systems to enhance noise it can use a certain amount of this special filtering mechanism is the traditional linear filter is not available. Therefore, 1DSR has a wide application prospect for the weak signal extraction of signal frequency band and noise frequency band overlap. However, because of its own properties similar to a low pass filter, 1DSR still needs to be further improved in the filtering effect. In this paper, a weak signal detection algorithm based on noise enhancing periodic signal, called two-dimensional stochastic resonance, is studied, and the algorithm is applied in bearing fault diagnosis. This paper analyzes and discusses the weak signal extraction algorithm for discrete stochastic resonance, on this basis, we put forward a new set of general average two-dimensional stochastic resonance (ensemble average two-dimensional stochastic resonance E2DSR) algorithm, E2DSR method is the use of two-dimensional stochastic resonance and ensemble average method of combining together, to construct the E2DSR model. The output signal to noise ratio (signal-noise ratio, SNR) of the E2DSR model is also higher than that of 1DSR, and the characteristic frequency of fault can be prominently highlighted in the power spectrum. Simulation experiments and bearing data experimental results verify the advantages of the E2DSR algorithm. The new algorithm has more efficient noise suppression performance than 1DSR, and has excellent ability of noise elimination and weak signal detection. In order to further study the two-dimensional stochastic resonance, this paper proposes a two-dimensional adaptive stochastic resonance (two-dimensional complementary stochastic complementary resonance 2DCSR) method, the band-pass filter and the resonance demodulation of bearing signal collected by the sensor, then the envelope signal is divided into two sections on the letter as the input of 2DCSR, using the weighted power output signal the spectral kurtosis (weighted power spectrum kurtosis, WPSK) parameter adaptive system to achieve optimum parameters, then the use of two output two-dimensional stochastic resonance, one output signal enhancement another output signal, then get the optimal output and power spectrum, used to identify bearing fault types. The numerical simulation and experimental results, as well as the comparison of the WPSK value of the output signals, show that 2DCSR can effectively improve the effect of bearing fault diagnosis. To sum up, this paper studies the application of noise enhanced weak signal detection and fault diagnosis in bearing fault diagnosis based on improved two-dimensional complementary random resonance (2-D). The two methods proposed in this paper are compared with the traditional 1DSR method under the equal conditions, and have the outstanding advantages of suppressing strong noise, good filtering effect and easy implementation. At the same time, the actual fault signal also proves the superiority and practicability of the improved two dimensional complementary stochastic resonance method.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH133.3

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