發(fā)布時間：2018-04-27 10:44

  本文選題：故障診斷 + 參數(shù)估計(jì)��； 參考：《西南交通大學(xué)》2015年碩士論文

【摘要】：本文為實(shí)現(xiàn)高速列車運(yùn)行時轉(zhuǎn)向架關(guān)鍵部位的參數(shù)辨識和故障診斷,對高速列車動力學(xué)模型關(guān)鍵參數(shù)估計(jì)方法進(jìn)行研究,將基于非線性濾波器的狀態(tài)估計(jì)方法應(yīng)用于高速列車關(guān)鍵參數(shù)估計(jì)中,主要包括以下幾個方面的研究內(nèi)容：首先回顧了在狀態(tài)參數(shù)聯(lián)合估計(jì)領(lǐng)域以及高速列車關(guān)鍵參數(shù)估計(jì)領(lǐng)域中前人所做的有意義的工作,并討論了運(yùn)用卡爾曼濾波器、擴(kuò)展卡爾曼濾波器以及粒子濾波器來解決相應(yīng)參數(shù)檢測問題的可能性。接下來,針對列車動力學(xué)模型,參考實(shí)際參數(shù),建立了CRH380A列車橫向動力學(xué)模型,將白噪聲激擾作為模型輸入,相應(yīng)傳感器觀測結(jié)果作為模型輸出。為了驗(yàn)證模型的有效性,將實(shí)際列車振動平臺數(shù)據(jù)與高速列車模型數(shù)據(jù)進(jìn)行分析與對比。在確認(rèn)模型準(zhǔn)確可靠的基礎(chǔ)上,設(shè)定系統(tǒng)相應(yīng)的統(tǒng)計(jì)學(xué)數(shù)值,采集模型輸出,使用擴(kuò)展卡爾曼濾波器(EKF)以及邊緣粒子濾波器(Rao-Blackwellised粒子濾波器,RBPF)進(jìn)行參數(shù)估計(jì),觀測并比較了兩種濾波器的參數(shù)估計(jì)結(jié)果,分析了各自的性能優(yōu)劣勢。最后,由于參數(shù)估計(jì)體系采用線性列車模型并運(yùn)用高斯白噪聲模擬列車噪聲輸入,在實(shí)際檢測中不具備良好的適應(yīng)性,在Rao-Blackwellised粒子濾波器的基礎(chǔ)上,根據(jù)狀態(tài)擴(kuò)展理論對Rao-Blackwellised算法進(jìn)行改進(jìn),以解決原先算法中對于非線性非高斯信號適應(yīng)性差的問題。運(yùn)用了狀態(tài)擴(kuò)展理論對列車實(shí)際運(yùn)行中軌道不平順的影響進(jìn)行了定量分析,并將其納入算法中進(jìn)行迭代。運(yùn)用該改進(jìn)算法,較好地估計(jì)出了列車在實(shí)際運(yùn)行中轉(zhuǎn)向架二系橫向阻尼系數(shù)、抗蛇行阻尼系數(shù)和輪對踏面錐度等幾個參數(shù)。估計(jì)結(jié)果較原始的Rao-Blackwellised濾波器在參數(shù)估計(jì)精度上有一定提升。接著模擬了多種可能發(fā)生的列車運(yùn)行故障,使用改進(jìn)后的方法估計(jì)目標(biāo)參數(shù),結(jié)果表明改進(jìn)的參數(shù)估計(jì)方法對實(shí)際噪聲具有良好的適應(yīng)性。
[Abstract]:In order to realize the parameter identification and fault diagnosis of the key parts of the bogie when the high-speed train is running, the method of estimating the key parameters of the dynamic model of the high-speed train is studied in this paper. The state estimation method based on nonlinear filter is applied to estimate the key parameters of high-speed train. The main contents are as follows: firstly, the important work done in the field of joint estimation of state parameters and the estimation of key parameters of high-speed trains is reviewed, and the application of Kalman filter is discussed. Extend Kalman filter and particle filter to solve the problem of parameter detection. Then, according to the train dynamics model and referring to the actual parameters, the CRH380A train lateral dynamics model is established. The white noise excitation is taken as the input of the model, and the corresponding sensor observation results are taken as the model output. In order to verify the validity of the model, the actual train vibration platform data and the high-speed train model data are analyzed and compared. On the basis of confirming the accuracy and reliability of the model, the corresponding statistical values of the system are set, the output of the model is collected, and the parameters are estimated by using the extended Kalman filter (EKF) and the edge particle filter (Rao-Blackwellised particle filter (RBPF). The parameter estimation results of the two filters are observed and compared, and their performance is analyzed. Finally, because the parameter estimation system adopts the linear train model and uses Gao Si white noise to simulate the train noise input, it has no good adaptability in the actual detection. Based on the Rao-Blackwellised particle filter, According to the state expansion theory, the Rao-Blackwellised algorithm is improved to solve the problem of poor adaptability to nonlinear non- signals in the original algorithm. The influence of track irregularity in actual train operation is analyzed quantitatively by using the state expansion theory, and it is incorporated into the algorithm to iterate. By using the improved algorithm, several parameters, such as the transverse damping coefficient of the second system of the bogie, the anti-snake damping coefficient and the taper of the wheelset tread, are well estimated in the actual operation of the bogie. The estimation result is better than the original Rao-Blackwellised filter in parameter estimation accuracy. Then several possible train faults are simulated and the target parameters are estimated by using the improved method. The results show that the improved method has a good adaptability to actual noise.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U270.33;TN713

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本文編號：1810412