本文選題：球磨機(jī)料位　切入點(diǎn)：梅爾頻率倒譜系數(shù)　出處：《太原理工大學(xué)》2015年碩士論文

【摘要】：鋼球磨煤機(jī)是國(guó)內(nèi)廣泛用于火力發(fā)電廠制粉系統(tǒng)的關(guān)鍵設(shè)備。其是否能夠正常運(yùn)行并且是否運(yùn)行在最佳工況是影響制粉系統(tǒng)工作效率的重要因素。因此，球磨機(jī)料位的準(zhǔn)確測(cè)量是實(shí)現(xiàn)優(yōu)化控制、安全生產(chǎn)和節(jié)能降耗的關(guān)鍵。 由于球磨機(jī)一般工作在旋轉(zhuǎn)和密閉狀態(tài)，無(wú)法直接進(jìn)行料位測(cè)量，一般通過(guò)間接法進(jìn)行檢測(cè)。根據(jù)軟測(cè)量思想，可以通過(guò)建立輔助變量及其特征參數(shù)與主導(dǎo)變量之間的模型來(lái)估計(jì)待測(cè)變量的值。研究發(fā)現(xiàn)，球磨機(jī)噪音信號(hào)及振動(dòng)信號(hào)是與球磨機(jī)料位變化密切相關(guān)的變量，所以本研究以球磨機(jī)噪音信號(hào)及振動(dòng)信號(hào)作為輔助變量，建立軟測(cè)量模型。在對(duì)軟測(cè)量模型輸入輔助信號(hào)進(jìn)行分析與處理時(shí)，常用的是功率譜分析方法，本文引入梅爾頻率倒譜系數(shù)，它是基于人耳聽覺特性提出的，能夠更好的模擬人耳對(duì)球磨機(jī)噪音信號(hào)的識(shí)別，并且具有計(jì)算方便且實(shí)用性較強(qiáng)的優(yōu)點(diǎn)，為實(shí)現(xiàn)通過(guò)球磨機(jī)噪音信號(hào)反映料位提供了可靠依據(jù)。 通過(guò)對(duì)球磨機(jī)的振動(dòng)和振聲信號(hào)進(jìn)行分析，發(fā)現(xiàn)其具有強(qiáng)隨機(jī)和不確定性的特點(diǎn)，因此本文引入云模型，它對(duì)不確定性問(wèn)題具有很強(qiáng)的處理能力。云模型系統(tǒng)能夠?qū)崿F(xiàn)輸入論域到輸出論域的函數(shù)映射，并且以云理論為基礎(chǔ)的虛擬云、綜合云算法可以解決規(guī)則缺失及規(guī)則精簡(jiǎn)的問(wèn)題。 針對(duì)單一信息反映料位的局限性，以球磨機(jī)噪音信號(hào)及振動(dòng)信號(hào)作為系統(tǒng)輸入，采用基于二維云模型不確定性推理信息融合的方法建立球磨機(jī)料位軟測(cè)量系統(tǒng)。本文的主要研究工作包括： (1)針對(duì)加速度傳感器和音頻傳感器采集到球磨機(jī)軸承振動(dòng)信號(hào)和噪聲信號(hào)，分別采用功率譜分析方法和MFCC方法進(jìn)行信號(hào)處理與分析； (2)根據(jù)逆向云算法得到構(gòu)建云推理系統(tǒng)前件的概念云模型數(shù)值特征，，并結(jié)合推理機(jī)制給出相應(yīng)的后件云參數(shù)，完成云推理規(guī)則庫(kù)的建立； (3)以單獨(dú)的音頻信號(hào)或振動(dòng)信號(hào)作為輔助變量，采用一維云推理建立軟測(cè)量模型，并利用虛擬云算法完成不充足樣本類下的稀疏規(guī)則推理； (4)以兩個(gè)傳感器下的信號(hào)為輔助變量，結(jié)合二維云推理建立軟測(cè)量系統(tǒng)，實(shí)現(xiàn)信息融合，并利用綜合云算法進(jìn)行規(guī)則精簡(jiǎn)。 實(shí)驗(yàn)結(jié)果表明，二維云推理實(shí)驗(yàn)相對(duì)于一維云推理實(shí)驗(yàn)，其測(cè)量精度更高，并且與其他信息融合算法相比也具有一定的優(yōu)勢(shì)。本方法的測(cè)量精度能夠滿足現(xiàn)場(chǎng)測(cè)量應(yīng)用的需求。
[Abstract]:Ball mill is the key equipment widely used in the pulverizing system of thermal power plant in our country. Whether it can run normally and in the best working condition is an important factor affecting the efficiency of pulverizing system. Accurate measurement of material level of ball mill is the key to realize optimal control, safe production and energy saving and consumption reduction. Because the ball mill generally works in the state of rotation and sealing, it can not measure the material level directly, and generally it is detected by indirect method. The values of the variables to be tested can be estimated by establishing a model between the auxiliary variables, their characteristic parameters and the dominant variables. It is found that the noise and vibration signals of the ball mill are closely related to the change of the material level of the ball mill. So in this study, the noise and vibration signals of ball mill are taken as auxiliary variables, and the soft sensor model is established. In the analysis and processing of the input auxiliary signal of the soft sensor model, the power spectrum analysis method is commonly used. In this paper, the Mel frequency cepstrum is introduced, which is based on the auditory characteristics of the human ear. It can better simulate the recognition of the noise signal of the ball mill by the human ear, and has the advantages of convenient calculation and strong practicability. It provides a reliable basis for reflecting the material level through the noise signal of ball mill. Through the analysis of vibration and vibration signal of ball mill, it is found that it has the characteristics of strong randomness and uncertainty, so the cloud model is introduced in this paper. It has a strong ability to deal with uncertain problems. The cloud model system can realize the functional mapping from input domain to output domain and the virtual cloud based on cloud theory. Synthesis cloud algorithm can solve the problem of rule deficiency and rule reduction. Aiming at the limitation of single information reflecting material level, the noise signal and vibration signal of ball mill are used as system input. The soft sensor system of ball mill material level is established by using the method of fusion of uncertain reasoning information based on two-dimensional cloud model. The main research work of this paper is as follows:. 1) aiming at the vibration signal and noise signal of ball mill bearing collected by accelerometer and audio sensor, the power spectrum analysis method and MFCC method are used to process and analyze the signal. (2) according to the reverse cloud algorithm, the numerical features of the conceptual cloud model of the former part of the cloud inference system are obtained, and the corresponding cloud parameters of the latter part are given in combination with the reasoning mechanism, and the cloud inference rule base is established. (3) taking individual audio signal or vibration signal as auxiliary variable, using one-dimensional cloud reasoning to establish soft sensor model, and using virtual cloud algorithm to complete sparse rule reasoning under insufficient sample class. Taking the signal under two sensors as the auxiliary variable and combining with two-dimensional cloud reasoning, a soft sensor system is established to realize information fusion, and the rules are reduced by using the synthetic cloud algorithm. The experimental results show that the measurement accuracy of two-dimensional cloud reasoning experiment is higher than that of one-dimensional cloud reasoning experiment. Compared with other information fusion algorithms, the measurement accuracy of this method can meet the needs of field measurement applications.
【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】：2015
【分類號(hào)】：TQ051.9

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