本文選題：內(nèi)燃機(jī) + 噪聲源識(shí)別�。� 參考：《吉林大學(xué)》2014年碩士論文

【摘要】：NVH(Noise Vibration and Harshness)水平是衡量機(jī)動(dòng)車輛性能的重要指標(biāo)之一，NVH直接反應(yīng)機(jī)動(dòng)車輛舒適性品質(zhì)，而機(jī)動(dòng)車輛舒適性程度直接影響該車輛的市場(chǎng)競(jìng)爭(zhēng)力。車輛的噪聲和振動(dòng)特性的研究和控制重點(diǎn)在于對(duì)內(nèi)燃機(jī)噪聲進(jìn)行控制，而噪聲源識(shí)別對(duì)內(nèi)燃機(jī)噪聲控制起重要的作用。要想合理的控制內(nèi)燃機(jī)噪聲，應(yīng)該對(duì)內(nèi)燃機(jī)的振動(dòng)噪聲產(chǎn)生原因進(jìn)行研究，，即對(duì)振動(dòng)噪聲的產(chǎn)生部位進(jìn)行定位，準(zhǔn)確測(cè)量和分析振源的特性，如振源的類型、頻率特性、聲壓級(jí)大小、變化和傳播規(guī)律等。然后根據(jù)振動(dòng)噪聲源的分析結(jié)果，采取相應(yīng)的措施，降低內(nèi)燃機(jī)的輻射聲壓級(jí)。近三十年來，內(nèi)燃機(jī)科技工作者通過不懈的努力，使其在低噪聲結(jié)構(gòu)優(yōu)化設(shè)計(jì)、低噪聲燃燒系統(tǒng)開發(fā)、內(nèi)燃機(jī)噪聲分離以及阻尼降噪技術(shù)等領(lǐng)域均有多項(xiàng)技術(shù)突破，有關(guān)資料顯示，與三十年前噪聲整體輻射水平相對(duì)比，其總體噪聲輻射水平下降了12dB。 即便如此，內(nèi)燃機(jī)噪聲輻射水平仍有下降的空間和必要性。由于內(nèi)燃機(jī)屬于復(fù)雜機(jī)械系統(tǒng)，當(dāng)內(nèi)燃機(jī)系統(tǒng)運(yùn)行時(shí)，其內(nèi)部多個(gè)零部件在同時(shí)運(yùn)轉(zhuǎn)，這使待分析的目標(biāo)部件響應(yīng)即使可以直接測(cè)量也受到非目標(biāo)零部件振動(dòng)信號(hào)的影響，在頻譜中多個(gè)振源信號(hào)往往存在重疊，再加上實(shí)驗(yàn)手段也十分復(fù)雜，實(shí)驗(yàn)成本高，這使得傳統(tǒng)的內(nèi)燃機(jī)噪聲源識(shí)別技術(shù)具有一定的局限性。隨著新理論的提出以及新技術(shù)的應(yīng)用，采用現(xiàn)代信號(hào)處理方法對(duì)內(nèi)燃機(jī)的噪聲和振動(dòng)特性的研究有著重要的理論意義和工程實(shí)用價(jià)值。 針對(duì)上述相關(guān)問題，本文開展了相應(yīng)的研究工作： 首先，闡述了課題研究背景，介紹柴油機(jī)振聲特性的危害性，以及當(dāng)前柴油機(jī)振動(dòng)信號(hào)處理過程中存在的技術(shù)問題。詳盡介紹了盲分離算法發(fā)展過程以及研究現(xiàn)狀，內(nèi)燃機(jī)振動(dòng)信號(hào)現(xiàn)代處理方法，并指出應(yīng)用時(shí)域盲識(shí)別算法對(duì)內(nèi)燃機(jī)噪聲源識(shí)別理論意義及工程價(jià)值； 其次，利用連續(xù)小波變換對(duì)內(nèi)燃機(jī)表面振動(dòng)信號(hào)進(jìn)行時(shí)-頻分析，通過對(duì)內(nèi)燃機(jī)各個(gè)部位表面加速度振動(dòng)信號(hào)的特征的分解，對(duì)內(nèi)燃機(jī)在運(yùn)轉(zhuǎn)過程中的主要噪聲源進(jìn)行識(shí)別，為內(nèi)燃機(jī)的噪聲源的識(shí)別研究提供一些方法和途徑； 再次，建立了源信號(hào)線性瞬時(shí)混合模型和源信號(hào)線性卷積混合模型，介紹了有關(guān)信息論的基本概念，介紹了線性瞬時(shí)盲分離問題的常用解混算法，并詳細(xì)的介紹了FastICA算法的流程，通過數(shù)值算例驗(yàn)證了FastICA算法的高效性和魯棒性。同時(shí)，介紹線性卷積盲分離問題的常用解混算法，詳盡介紹了MBLMS算法流程，并通過數(shù)值算例驗(yàn)證MBLMS算法的高效性和魯棒性。 最后，應(yīng)用FastICA算法以及改進(jìn)的MBLMS算法對(duì)柴油機(jī)表面混合信號(hào)進(jìn)行分離，通過分離結(jié)果比較驗(yàn)證了改進(jìn)的MBLMS算法的高效性和可靠性，也進(jìn)一步對(duì)內(nèi)燃機(jī)非平穩(wěn)振聲響應(yīng)結(jié)構(gòu)模型進(jìn)行了驗(yàn)證。
[Abstract]:The level of NVH noise Vibration and Harshnessis is one of the important indexes to measure the performance of motor vehicles, which directly reflects the comfort quality of motor vehicles, and the degree of comfort of motor vehicles directly affects the market competitiveness of motor vehicles. The research and control of vehicle noise and vibration characteristics is focused on the control of internal combustion engine noise, and noise source identification plays an important role in internal combustion engine noise control. In order to control the internal combustion engine noise reasonably, it is necessary to study the cause of the vibration noise, that is, to locate the position of the vibration noise, to measure and analyze the characteristics of the vibration source, such as the type of the vibration source and the frequency characteristic. Sound pressure level, variation and propagation law, etc. Then according to the analysis results of vibration and noise source, the corresponding measures are taken to reduce the sound pressure level of internal combustion engine. In the past 30 years, the scientific and technological workers of internal combustion engine have made many technical breakthroughs in the fields of low noise structure optimization design, low noise combustion system development, internal combustion engine noise separation and damping noise reduction technology through unremitting efforts. The relative data show that compared with the total noise radiation level of 30 years ago, the overall noise radiation level has decreased by 12 dB. Even so, the internal combustion engine noise radiation level still has the space and the necessity. Since the internal combustion engine belongs to a complex mechanical system, when the internal parts of the internal combustion engine system are running at the same time, the response of the target component to be analyzed is affected by the vibration signal of the non-target component even if it can be measured directly. There is often overlap in the spectrum of multiple vibration source signals, in addition, the experimental means are also very complex and the experimental cost is high, which makes the traditional identification technology of internal combustion engine noise source has some limitations. With the development of new theory and the application of new technology, it is of great theoretical significance and practical value to study the noise and vibration characteristics of internal combustion engine by using modern signal processing method. In view of the above related problems, this paper has carried out the corresponding research work: first, elaborated the research background, introduced the diesel engine vibration sound characteristic harmfulness, And the technical problems existing in the process of diesel engine vibration signal processing. The development and research status of blind separation algorithm and the modern processing method of internal combustion engine vibration signal are introduced in detail, and the theoretical significance and engineering value of using time domain blind recognition algorithm to identify internal combustion engine noise source are pointed out. The time-frequency analysis of internal combustion engine surface vibration signal is carried out by using continuous wavelet transform, and the main noise sources of internal combustion engine during operation are identified by decomposing the characteristics of the acceleration vibration signal on each part of the internal combustion engine. It provides some methods and approaches for the identification of internal combustion engine noise sources. Thirdly, a linear instantaneous mixing model of source signals and a mixed model of linear convolution of source signals are established, and the basic concepts of information theory are introduced. In this paper, the common unmixing algorithms for linear instantaneous blind separation are introduced, and the flow chart of FastICA algorithm is introduced in detail. The efficiency and robustness of FastICA algorithm are verified by numerical examples. At the same time, the common de-mixing algorithms for linear convolution blind separation problem are introduced, and the flow chart of MBLMS algorithm is introduced in detail, and the efficiency and robustness of MBLMS algorithm are verified by numerical examples. Finally, the FastICA algorithm and the improved MBLMS algorithm are used to separate the diesel engine surface mixed signals. The efficiency and reliability of the improved MBLMS algorithm are verified by comparing the separation results. The structural model of non-stationary vibration response of internal combustion engine is also verified.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TB535

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本文編號(hào)：2025407