本文選題：鋁電解 + 異常診斷系統(tǒng)�。� 參考：《北方工業(yè)大學》2017年碩士論文

【摘要】：現(xiàn)代鋁生產(chǎn)的主要方法是基于氧化鋁—冰晶石的電解法。該方法由于內(nèi)部復雜的物理、化學變化,以及外部各種場的存在,形成了復雜的槽況特征。建立有效的故障診斷系統(tǒng)不但可以提高生產(chǎn)過程中鋁的質(zhì)量和產(chǎn)量,同時也能降低電能消耗,對鋁的生產(chǎn)有重要意義。目前,國內(nèi)外都對鋁電解槽槽況診斷技術進行了大量研究,提出了基于解析模型的故障診斷方法和基于知識的故障診斷方法。由于鋁生產(chǎn)過程中數(shù)據(jù)獲取比較困難并且我國鋁電解工藝和整流設備與國外相比還有一定差距,使一些方法在我國無法推廣。針對這一問題,本文研究了一種基于陽極電流和集成神經(jīng)網(wǎng)絡的故障診斷方法。陽極電流中包含了大量的槽況信息,通過對陽極電流的分析,可以為鋁電解槽槽況的診斷提供依據(jù)。在本文中,首先對陽極電流信號進行頻譜分析,通過計算每一段頻譜上的香濃熵提取其特征值,作為子神經(jīng)網(wǎng)絡1的輸入,然后計算陽極電流的均值、方差、偏度和峰度作為子神經(jīng)網(wǎng)絡2的輸入,對子神經(jīng)網(wǎng)絡1和子神經(jīng)網(wǎng)絡2的輸出的加權平均值作為決策融合神經(jīng)網(wǎng)絡的輸入。由于單神經(jīng)網(wǎng)絡的使用效果和使用者的經(jīng)驗有很大關系,同時當出現(xiàn)新的故障或者有新的特征值時很難擴展,所以本文采用集成神經(jīng)網(wǎng)絡來提高診斷系統(tǒng)的泛化能力。在本文中,首先對子神經(jīng)網(wǎng)絡1和子神經(jīng)網(wǎng)絡2進行并聯(lián),使他們之間的診斷相互獨立,然后將并聯(lián)后的網(wǎng)絡和決策融合神經(jīng)網(wǎng)絡進行串聯(lián)來完成對槽況的診斷。本文還通過python建立了一個鋁電解槽槽況診斷系統(tǒng),首先通過網(wǎng)絡對采集到的陽極電流進行讀取,并存入sqlite數(shù)據(jù)庫,然后對數(shù)據(jù)進行預處理,提取相應的特征值,最后經(jīng)過集成神經(jīng)網(wǎng)絡對槽況進行診斷。通過測試,該系統(tǒng)可以完成對鋁電解槽槽況的診斷。
[Abstract]:The main method of modern aluminum production is based on alumina-cryolite electrolysis. Because of the complex physical and chemical changes in the interior and the existence of various external fields, the method forms complex channel characteristics. The establishment of an effective fault diagnosis system can not only improve the quality and output of aluminum in the production process, but also reduce the power consumption, which is of great significance to the production of aluminum. At present, a great deal of research has been done on the condition diagnosis technology of aluminum reduction cell at home and abroad, and a fault diagnosis method based on analytical model and a method based on knowledge are put forward. Because of the difficulty of data acquisition in aluminum production and the gap between China's aluminum electrolysis process and rectifier equipment compared with foreign countries, some methods can not be popularized in China. To solve this problem, a fault diagnosis method based on anode current and integrated neural network is studied in this paper. The anodic current contains a great deal of information about the status of the cell. The analysis of the anodic current can provide the basis for the diagnosis of the condition of the aluminum reduction cell. In this paper, the anodic current signal is analyzed by spectrum analysis, and the eigenvalue of the anodic current signal is extracted by calculating the aroma entropy of each section of the spectrum, which is used as the input of the subneural network 1, and then the mean value and variance of the anode current are calculated. Skewness and kurtosis are the input of subneural network 2, and the weighted average of the output of subneural network 1 and subneural network 2 is the input of decision fusion neural network. Since the use effect of single neural network is closely related to the user's experience and it is difficult to extend it when new faults or new eigenvalues occur, the integrated neural network is used to improve the generalization ability of diagnostic system. In this paper, the sub-neural network 1 and the sub-neural network 2 are connected in parallel to make the diagnosis independent of each other, and then the parallel network and the decision fusion neural network are connected in series to complete the diagnosis of the slot condition. In this paper, an aluminum reduction cell condition diagnosis system is established by python. Firstly, the anode current collected is read through the network, then stored in the sqlite database, and then the data is pretreated to extract the corresponding characteristic value. Finally, the slot condition is diagnosed by integrated neural network. Through testing, the system can complete the diagnosis of aluminum reduction cell condition.
【學位授予單位】：北方工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TF821;TP277

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