本文選題：乳化機 + EMD��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：乳化炸藥作為一種新式環(huán)保型炸藥,具有爆轟猛度強、抗水性良好等特點。同時乳化炸藥生產(chǎn)工藝簡單、產(chǎn)能大、生產(chǎn)成本低使得它在我國民爆行業(yè)得到了廣泛應(yīng)用。乳化機是乳化炸藥連續(xù)化、自動化生產(chǎn)線的核心設(shè)備,它是一種改良的旋轉(zhuǎn)機械設(shè)備,且具有高速旋轉(zhuǎn)的特性。要是內(nèi)部器械運轉(zhuǎn)異常,不僅會破壞生產(chǎn)工序的連續(xù)性能,影響產(chǎn)能和質(zhì)量,嚴重情況下還會發(fā)生機毀人亡的安全事故,造成巨大的經(jīng)濟損失和社會影響。為了能準確地檢測出乳化機潛藏的故障,提高維修效率,保證設(shè)備安全,本文研制了一套乳化機故障檢測和診斷系統(tǒng),并在實際生產(chǎn)中得到成功應(yīng)用。本文主要針對轉(zhuǎn)子故障、軸承故障等常見故障類型,在研究各類型的故障機理和發(fā)生征兆的基礎(chǔ)上,首先提出基于樣本熵的振動信號故障特征提取方法。針對樣本熵對原始信號獲取有限,故障特征區(qū)分度不高的缺陷,提出經(jīng)驗?zāi)Ｊ椒纸夥椒?Empirical Mode Decomposition,簡稱EMD)預(yù)處理樣本熵的故障特征提取方法。該方法利用EMD先把振動信號分解為若干個固有模態(tài)函數(shù)(Intrinsic Mode Function,簡稱IMF),然后選取若干具有代表性的IMF分量,將這些分量的樣本熵組成向量作為故障特征。EMD方法能將蘊藏在信號內(nèi)部的信息挖掘出來,有效克服樣本熵對信息獲取的局限性。結(jié)果表明,EMD結(jié)合樣本熵的方法不僅能夠區(qū)分不同類型的故障種類,還能提高了識別系統(tǒng)的容錯率。神經(jīng)網(wǎng)絡(luò)具備強非線性映射,以及自學(xué)習(xí)、自組織和自適應(yīng)的能力。將提取的故障特征作為BP神經(jīng)網(wǎng)絡(luò)的輸入,通過整理乳化機正常和故障的振動歷史數(shù)據(jù),分別構(gòu)造了振動特征參數(shù)的正常及故障狀態(tài)的訓(xùn)練樣本,并用訓(xùn)練好的神經(jīng)網(wǎng)絡(luò)進行故障類型識別。結(jié)果表明,BP神經(jīng)網(wǎng)絡(luò)能快速地識別出滾動軸承的故障類型,診斷效果良好。在乳化炸藥生產(chǎn)線現(xiàn)有設(shè)備的基礎(chǔ)上完成硬件配置,基于工業(yè)控制軟件組態(tài)王平臺實現(xiàn)對PLC的數(shù)據(jù)交互,通過VB調(diào)用MATLAB神經(jīng)網(wǎng)絡(luò)功能實現(xiàn)上位機故障診斷系統(tǒng)的開發(fā)。實踐結(jié)果表明,本文構(gòu)建的乳化機故障診斷系統(tǒng)能夠根據(jù)實際數(shù)據(jù)準確地識別出乳化機的故障類型,診斷準確率高,實際應(yīng)用效果好。
[Abstract]:Emulsion explosive, as a new type of environmental protection explosive, has the characteristics of strong detonation intensity and good water resistance. At the same time, emulsified explosive has been widely used in our country because of its simple production process, large production capacity and low production cost. Emulsifying machine is the core equipment of continuous and automatic production line of emulsion explosive. It is an improved rotating machine and has the characteristics of high speed rotation. If the operation of internal instruments is abnormal, it will not only destroy the continuous energy of production process, but also affect the production capacity and quality. In serious cases, the safety accident of machine destruction and death will occur, resulting in huge economic loss and social impact. In order to accurately detect the hidden faults of emulsifying machine, improve the maintenance efficiency and ensure the safety of the equipment, a fault detection and diagnosis system for emulsifying machine has been developed in this paper, and has been successfully applied in practical production. This paper mainly aims at the common fault types such as rotor fault bearing fault and so on. On the basis of studying the fault mechanism and occurrence symptom of each type a method of vibration signal fault feature extraction based on sample entropy is proposed. Aiming at the defects of limited sample entropy acquisition and low fault feature differentiation, an empirical Mode decomposition (EMD) preprocessing sample entropy method for fault feature extraction is proposed. In this method, the vibration signal is first decomposed into intrinsic Mode functions by EMD, and then some representative IMF components are selected. The sample entropy component vector of these components can be used as fault feature. EMD method can mine the information hidden inside the signal and overcome the limitation of sample entropy to information acquisition. The results show that EMD combined with sample entropy can not only distinguish different types of faults, but also improve the fault tolerance of the identification system. Neural networks have strong nonlinear mapping, as well as self-learning, self-organization and adaptive capabilities. Taking the extracted fault features as the input of BP neural network, the normal and fault training samples of the vibration characteristic parameters are constructed by sorting out the history data of the normal and fault vibration of the emulsifier. The trained neural network is used to identify the fault types. The results show that BP neural network can quickly identify the fault types of rolling bearings, and the diagnosis effect is good. The hardware configuration is completed on the basis of the existing equipment in the emulsion explosive production line, the data exchange to PLC is realized based on the industrial control software Kingview platform, and the development of the upper computer fault diagnosis system is realized by calling the function of MATLAB neural network in VB. The practical results show that the emulsifying machine fault diagnosis system constructed in this paper can accurately identify the fault types of the emulsifier according to the actual data. The diagnosis accuracy is high and the practical application effect is good.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TQ560.5;TP183;TP277

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