發(fā)布時(shí)間：2018-04-21 20:48

  本文選題：模態(tài)參數(shù)識(shí)別 + 懸架系統(tǒng)��； 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：近年來(lái),人們對(duì)汽車(chē)平順性和安全性的要求越來(lái)越高。懸架系統(tǒng)作為直接影響車(chē)輛安全性、平順性和操穩(wěn)性的重要部件,如果其發(fā)生故障可能會(huì)造成嚴(yán)重的經(jīng)濟(jì)與人身安全損失。統(tǒng)計(jì)發(fā)現(xiàn),不同故障原因類(lèi)型中懸架部件損壞造成的故障比率非常高。因此,有必要對(duì)懸架的故障診斷及在線(xiàn)監(jiān)測(cè)進(jìn)行深入的研究,從而降低懸架故障導(dǎo)致的損失。本研究致力于研究對(duì)懸架系統(tǒng)的在線(xiàn)監(jiān)測(cè)方法,從而及時(shí)發(fā)現(xiàn)懸架早期故障并加以排除來(lái)有效地降低事故發(fā)生的概率,最大限度地保證行車(chē)安全,并提高汽車(chē)的行駛平順性。但是由于路面情況復(fù)雜,路面不平度對(duì)輪胎的激勵(lì)一般為特定頻段的隨機(jī)激勵(lì),如何對(duì)懸架系統(tǒng)的運(yùn)行狀態(tài)進(jìn)行在線(xiàn)監(jiān)測(cè)一直是故障診斷領(lǐng)域的研究難點(diǎn)。隨機(jī)子空間法作為一種將響應(yīng)信號(hào)作為輸入的模態(tài)識(shí)別算法,具有信號(hào)采集方便、適用于隨機(jī)振動(dòng)狀態(tài)和高噪聲工況下魯棒性強(qiáng)等優(yōu)點(diǎn)受到了越來(lái)越多學(xué)者的關(guān)注。但其識(shí)別準(zhǔn)確性受阻尼比的影響較大,而懸架系統(tǒng)的阻尼比較高(在20%-30%之間),這限制了隨機(jī)子空間法在車(chē)輛懸架系統(tǒng)監(jiān)測(cè)中的應(yīng)用。因此本文提出將改進(jìn)后的平均相關(guān)子空間方法(ACS-SSI)應(yīng)用于懸架在線(xiàn)監(jiān)測(cè)中,采用多次平均后的相關(guān)函數(shù)信號(hào)取代原算法采用的響應(yīng)信號(hào)作為算法輸入,從而大大提升了算法在復(fù)雜工況下的識(shí)別精度。然后建立七自由度線(xiàn)性車(chē)輛振動(dòng)模型,仿真識(shí)別了在不同阻尼比、路面激勵(lì)及噪聲條件下的模態(tài)參數(shù),以此判斷其對(duì)識(shí)別結(jié)果的影響,并驗(yàn)證在懸架狀態(tài)監(jiān)測(cè)上應(yīng)用平均相關(guān)隨機(jī)子空間算法的合理性。其次,判斷各個(gè)模態(tài)參數(shù)對(duì)故障的靈敏度,并基于此判斷建立了基于振型和模態(tài)能量差異法作為依據(jù)的在線(xiàn)監(jiān)測(cè)方法。最后,設(shè)計(jì)傳感器布置方案對(duì)車(chē)身姿態(tài)的相關(guān)參數(shù)進(jìn)行采集,最終確定了利用9軸MEMS慣性傳感器采集車(chē)身垂向振動(dòng),車(chē)身俯仰角速度以及車(chē)身側(cè)傾角速度,并進(jìn)行懸架系統(tǒng)不同的故障形式的監(jiān)測(cè)試驗(yàn),從而驗(yàn)證了在線(xiàn)監(jiān)測(cè)算法的可信度。
[Abstract]:In recent years, the requirements of vehicle ride comfort and safety are becoming higher and higher. Suspension system is an important component which directly affects vehicle safety, ride comfort and operating stability. If it breaks down, it may cause serious economic and personal safety losses. According to statistics, the failure rate of suspension parts is very high in different fault cause types. Therefore, it is necessary to study deeply the fault diagnosis and on-line monitoring of suspension, so as to reduce the loss caused by suspension failure. This study is devoted to study the on-line monitoring method of suspension system, so as to find the early fault of suspension in time and remove it to reduce the probability of accident, to ensure the safety of driving to the maximum extent, and to improve the ride comfort of the vehicle. However, due to the complexity of pavement conditions, the road roughness to the tire excitation is generally random excitation in a specific frequency band, how to monitor the running state of suspension system online has been a difficulty in the field of fault diagnosis. As a modal recognition algorithm which takes response signal as input, stochastic subspace method has been paid more and more attention by more and more scholars because of its convenience in signal acquisition, good robustness under random vibration and high noise condition, and so on. However, the accuracy of identification is greatly affected by the damping ratio, and the damping ratio of suspension system is high (between 20% and 30%), which limits the application of stochastic subspace method in vehicle suspension system monitoring. In this paper, the improved average correlation subspace method (ACS-SSI) is applied to the on-line monitoring of suspension, and the response signal of the original algorithm is replaced by the multi-average correlation function signal as the input of the algorithm. Thus, the recognition accuracy of the algorithm under complex working conditions is greatly improved. Then the vibration model of a seven-degree-of-freedom linear vehicle is established, and the modal parameters under different damping ratio, road excitation and noise are identified, and the influence of the modal parameters on the identification results is judged. The rationality of applying the average correlation random subspace algorithm to suspension condition monitoring is verified. Secondly, the sensitivity of each modal parameter to the fault is judged, and an on-line monitoring method based on mode shape and modal energy difference method is established. Finally, the sensor layout scheme is designed to collect the relative parameters of the body attitude. Finally, the 9 axis MEMS inertial sensor is used to collect the vertical vibration of the body, the pitch angle velocity of the body and the roll angular velocity of the body. The reliability of the online monitoring algorithm is verified by testing the different fault forms of suspension system.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U463.33

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本文編號(hào)：1784040