本文選題：行星輪系 + 動力學(xué)分析��； 參考：《中國礦業(yè)大學(xué)》2017年碩士論文

【摘要】：像大功率采煤機(jī)這一類大型旋轉(zhuǎn)機(jī)械通常工作在低速重載、強(qiáng)噪聲背景環(huán)境下,從而導(dǎo)致在故障診斷過程中獲取的振動信號是被強(qiáng)噪聲背景深度污染的信噪比極低的信號,嚴(yán)重影響診斷的精確性。而對于大功率采煤機(jī)搖臂傳動系統(tǒng)中的低速級行星輪系而言,其擁有更低的轉(zhuǎn)頻,當(dāng)其發(fā)生故障時,有用的特征信息顯得更加微弱。因此,如何提高極端工況下微弱故障信號信噪比就成了故障診斷領(lǐng)域關(guān)鍵課題之一。為此,本文以行星輪系為研究對象,采用隨機(jī)共振方法對強(qiáng)噪聲背景下行星輪系微弱特征信息提取及故障診斷做出了研究,具體如下所示:1)對正常以及故障狀態(tài)下的行星輪系動力學(xué)特性進(jìn)行了對比分析。包括采用UG軟件對兩級行星輪系進(jìn)行三維建模,采用ADAMS軟件對兩級行星輪系進(jìn)行動力學(xué)特性分析。分析結(jié)果表明,故障狀態(tài)下時域波形中將產(chǎn)生沖擊現(xiàn)象,而頻域波形中邊頻帶增多幅值增加,且故障越嚴(yán)重越明顯。2)基于雙穩(wěn)態(tài)隨機(jī)共振理論,提出了自適應(yīng)雙穩(wěn)態(tài)隨機(jī)共振方法。該方法采用移頻變尺度對大參數(shù)信號進(jìn)行預(yù)處理,使其滿足隨機(jī)共振要求;采用改進(jìn)魚群算法對系統(tǒng)參數(shù)進(jìn)行同步優(yōu)化,尋找全局最優(yōu)值,并以改進(jìn)信噪比為優(yōu)化目標(biāo)。采用余弦仿真信號以及行星輪系動力學(xué)仿真數(shù)據(jù)對算法的有效性進(jìn)行驗證。分析結(jié)果表明,該算法可以將噪聲能量轉(zhuǎn)移到微弱特征信號上,提高改進(jìn)信噪比,且相對于EEMD以及小波閾值降噪方法而言更加的優(yōu)越。3)為了研究勢函數(shù)對隨機(jī)共振系統(tǒng)輸出的影響,尋找更加高效的微弱特征提取方法,基于多穩(wěn)態(tài)隨機(jī)共振理論,提出了自適應(yīng)多穩(wěn)態(tài)隨機(jī)共振方法。在只改變勢函數(shù)的基礎(chǔ)上,同樣采用余弦仿真信號以及行星輪系動力學(xué)仿真數(shù)據(jù)對算法的有效性進(jìn)行驗證。分析結(jié)果表明,多穩(wěn)態(tài)系統(tǒng)明顯優(yōu)于雙穩(wěn)態(tài)系統(tǒng),更有助于微弱特征信號的提取。4)通過實驗數(shù)據(jù)對所提出的方法進(jìn)行驗證。這里采集了井下正常狀態(tài)下的數(shù)據(jù),用于確定井下的工作環(huán)境。在實驗室采集實驗臺故障數(shù)據(jù),用于驗證所提出的算法。分析結(jié)果表明,所提出的方法在工程應(yīng)用方面具有一定的能力。對于極低信噪比環(huán)境而言,相對于雙穩(wěn)態(tài)系統(tǒng),多穩(wěn)態(tài)系統(tǒng)擁有更強(qiáng)的處理能力。5)文章對所做工作進(jìn)行了總結(jié),并對相關(guān)的研究技術(shù)進(jìn)行了展望。
[Abstract]:Large rotating machinery, such as high power shearer, usually works in low speed and heavy load, strong noise background environment, so the vibration signal obtained in the process of fault diagnosis is a signal with very low signal-to-noise ratio (SNR) polluted by the depth of strong noise background.The accuracy of diagnosis is seriously affected.For the low-speed planetary gear train in the rocker arm drive system of high-power shearer, it has lower rotation frequency, and the useful characteristic information is weaker when it breaks down.Therefore, how to improve the signal-to-noise ratio of weak fault signals under extreme conditions has become one of the key problems in fault diagnosis field.Therefore, taking planetary gear train as the research object, using stochastic resonance method, the weak characteristic information extraction and fault diagnosis of planetary gear train under strong noise background are studied.The dynamic characteristics of planetary gear trains under normal and fault conditions are compared and analyzed.UG software is used to model the two-stage planetary gear train, and ADAMS software is used to analyze the dynamic characteristics of the two-stage planetary gear train.The results show that impulse will occur in the time-domain waveform under the fault state, while the amplitude of the side band increases in the frequency-domain waveform, and the more serious the fault is, the more obvious the fault is. (2) based on the bistable stochastic resonance theory,An adaptive bistable stochastic resonance method is proposed.In this method, the large parameter signal is preprocessed with frequency shift and variable scale to satisfy the stochastic resonance requirement, and the system parameters are synchronously optimized by improved fish swarm algorithm to find the global optimal value, and the improved signal-to-noise ratio (SNR) is taken as the optimization target.The validity of the algorithm is verified by using cosine simulation signal and planetary gear train dynamics simulation data.The analysis results show that the proposed algorithm can transfer the noise energy to the weak characteristic signal and improve the signal-to-noise ratio (SNR).In order to study the effect of potential function on the output of stochastic resonance system and find a more efficient method of weak feature extraction, based on the theory of multi-steady state stochastic resonance, it is more superior than EEMD and wavelet threshold denoising method in order to study the effect of potential function on the output of stochastic resonance system.An adaptive multistable stochastic resonance method is proposed.On the basis of only changing the potential function, the validity of the algorithm is verified by using the cosine simulation signal and the planetary gear train dynamics simulation data.The analysis results show that the multistable system is better than the bistable system, and it is more helpful to extract the weak characteristic signal. 4) the proposed method is verified by the experimental data.Here the normal state of the acquisition of underground data, used to determine the working environment underground.The fault data were collected in the laboratory to verify the proposed algorithm.The analysis results show that the proposed method has certain ability in engineering application.For very low SNR environment, compared with bistable systems, multistable systems have stronger processing power. 5) in this paper, the work done is summarized, and the related research techniques are prospected.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD421.6

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