本文選題：空調(diào)　切入點(diǎn)：噪音分離　出處：《華中科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著我國現(xiàn)代化工業(yè)水平的提高,國家經(jīng)濟(jì)不斷向前發(fā)展,空調(diào)的使用率越來越高。在空調(diào)運(yùn)轉(zhuǎn)過程中,由空調(diào)部件振動(dòng)而其引起的噪聲蘊(yùn)含著空調(diào)運(yùn)行狀態(tài)的重要信息,但是多個(gè)部件的振動(dòng)和噪聲同時(shí)出現(xiàn),各類噪聲互相混合在一起導(dǎo)致很難提取到準(zhǔn)確有用的信息。因此,需要尋找一種比較理想的噪音分離方法,從采集到的混合噪音信號(hào)中提取出各個(gè)部件對應(yīng)的聲信號(hào),從而及時(shí)有效地監(jiān)測空調(diào)的狀態(tài)并準(zhǔn)確進(jìn)行故障診斷。獨(dú)立成分分析(independent component analysis,ICA)是近幾年發(fā)展起來的信號(hào)處理方法,這種方法不依據(jù)任何先驗(yàn)知識(shí),依據(jù)信號(hào)的統(tǒng)計(jì)特性,從觀測到的混合信號(hào)中恢復(fù)出獨(dú)立成分。首先簡單介紹了課題研究背景和意義,以及國內(nèi)外的研究現(xiàn)狀,對獨(dú)立成分分析理論基礎(chǔ)進(jìn)行了系統(tǒng)闡述,包括概率與統(tǒng)計(jì)知識(shí),信息論知識(shí),獨(dú)立成分分析的數(shù)學(xué)模型、可分離性條件等。然后研究了獨(dú)立成分分析的預(yù)處理過程:中心化過程和白化過程,實(shí)現(xiàn)了獨(dú)立成分分析中常用的兩個(gè)算法:信息最大化算法(Informax)和基于負(fù)熵最大化的快速算法(Fast ICA)。在Matlab平臺(tái)上用這兩個(gè)算法依次對正弦混合信號(hào),音頻混合信號(hào),噪音混合信號(hào)開展了分離實(shí)驗(yàn),從多個(gè)方面上對比分析了這兩個(gè)算法在分離混合信號(hào)的有效性。最后對負(fù)熵最大化的FastICA算法在空調(diào)噪音上的應(yīng)用作了進(jìn)一步探討,建立空調(diào)噪音分離的應(yīng)用模型,并通過實(shí)驗(yàn)提取出了不同部件的噪聲,獲得了較好的分離效果,驗(yàn)證了FastICA算法在空調(diào)噪音分離方面的可行性。
[Abstract]:With the development of modern industry and the development of national economy, the utilization rate of air conditioning is getting higher and higher. In the process of air conditioning operation, the noise caused by the vibration of air conditioning components contains important information of the operating state of air conditioning. But the vibration and noise of many parts appear at the same time, and all kinds of noise are mixed together to make it difficult to extract accurate and useful information. Therefore, it is necessary to find an ideal method of noise separation. The corresponding acoustic signals of each component are extracted from the collected mixed noise signals, so that the condition of air conditioning can be monitored and the fault diagnosis can be carried out in a timely and effective manner. Independent component Analysis (ICA) is a signal processing method developed in recent years. In this method, independent components are recovered from the observed mixed signals without any prior knowledge and according to the statistical characteristics of the signals. Firstly, the background and significance of the research are briefly introduced, as well as the current research situation at home and abroad. The theoretical basis of independent component analysis (ICA) is systematically expounded, including probability and statistics knowledge, information theory knowledge, mathematical model of independent component analysis, Then we studied the pretreatment process of independent component analysis: centralization process and whitening process. Two common algorithms in Independent component Analysis (ICA) are implemented: information maximization algorithm (Informax) and Fast Matlab algorithm based on negative entropy maximization. The two algorithms are used to mix sinusoidal signal and audio signal in turn on Matlab platform. The separation experiments of noise mixing signals are carried out, and the effectiveness of the two algorithms in separating mixed signals is compared and analyzed from several aspects. Finally, the application of FastICA algorithm with maximum negative entropy to air conditioning noise is further discussed. The application model of air conditioning noise separation is established, and the noise of different parts is extracted through experiments. The better separation effect is obtained, and the feasibility of FastICA algorithm in air conditioning noise separation is verified.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TB657.2

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