本文選題：智慧制造 + 復(fù)雜事件處理��； 參考：《華南理工大學(xué)》2016年博士論文

【摘要】：隨著云計(jì)算、物聯(lián)網(wǎng)、大數(shù)據(jù)、信息物理融合系統(tǒng)、企業(yè)2.0、工業(yè)4.0等的提出,信息技術(shù)與先進(jìn)制造技術(shù)深度融合,孕育出基于社會(huì)信息物理系統(tǒng)的智慧制造,形成一種面向服務(wù)、基于知識(shí)運(yùn)用的人機(jī)物協(xié)同制造模式。在智慧制造環(huán)境下,物聯(lián)網(wǎng)覆蓋整個(gè)生產(chǎn)車(chē)間,部署于車(chē)間的各種傳感器(如RFID、加速度計(jì)等)實(shí)時(shí)監(jiān)測(cè)整個(gè)生產(chǎn)過(guò)程,并通過(guò)網(wǎng)絡(luò)將數(shù)據(jù)傳送到處理中心。由于各種不確定因素,導(dǎo)致生產(chǎn)過(guò)程容易發(fā)生異常事件,造成生產(chǎn)過(guò)程的信息復(fù)雜且不易控制。需要對(duì)各種傳感器數(shù)據(jù)實(shí)時(shí)處理,挖掘出生產(chǎn)現(xiàn)場(chǎng)的異常事件,并預(yù)測(cè)將要發(fā)生的異常狀況,進(jìn)而基于實(shí)時(shí)與預(yù)測(cè)的異常事件,實(shí)現(xiàn)生產(chǎn)車(chē)間設(shè)備主動(dòng)調(diào)度,避免由于異常事件而給生產(chǎn)系統(tǒng)造成的危害。為此,本論文研究基于機(jī)械加工的工件異常事件監(jiān)測(cè)和刀具剩余壽命預(yù)測(cè)的主動(dòng)調(diào)度,包括如下主要內(nèi)容:(1)新型智慧制造模式分析與總結(jié)智慧裝備的特征,探討網(wǎng)絡(luò)融合與社會(huì)信息物理系統(tǒng)視角下的智慧制造模式,研究實(shí)現(xiàn)智慧制造的社會(huì)環(huán)境與關(guān)鍵共性技術(shù)問(wèn)題。(2)基于RFID的工件異常事件監(jiān)測(cè)構(gòu)建智慧制造車(chē)間的感知環(huán)境,定義各類(lèi)RFID事件模型,包括標(biāo)簽事件、簡(jiǎn)單事件和復(fù)雜事件等;給出復(fù)雜事件處理系統(tǒng)的框架,提出綜合的RFID數(shù)據(jù)清洗方法,實(shí)現(xiàn)面向?qū)崟r(shí)的工件異常事件監(jiān)測(cè),最后實(shí)驗(yàn)驗(yàn)證數(shù)據(jù)清洗方法和異常事件監(jiān)測(cè)的有效性。(3)基于無(wú)線(xiàn)加速度計(jì)的刀具狀態(tài)監(jiān)測(cè)給出刀具狀態(tài)監(jiān)測(cè)系統(tǒng)的框架,并搭建刀具狀態(tài)監(jiān)測(cè)的實(shí)驗(yàn)裝置;應(yīng)用小波變換去除振動(dòng)信號(hào)噪聲,用不同的方法提取信號(hào)在時(shí)域、頻域和時(shí)頻域的特征,并依據(jù)皮爾森相關(guān)系數(shù)選擇關(guān)鍵特征;建立神經(jīng)模糊網(wǎng)絡(luò)(Neuro-Fuzzy Networks,NFN)預(yù)測(cè)模型,編寫(xiě)刀具磨損與剩余壽命預(yù)測(cè)的人機(jī)接口程序,并與反向傳播神經(jīng)網(wǎng)絡(luò)、徑向基函數(shù)網(wǎng)絡(luò)相比較,驗(yàn)證NFN預(yù)測(cè)效果。(4)基于深度學(xué)習(xí)的刀具狀態(tài)監(jiān)測(cè)比較5種深度學(xué)習(xí)模型的結(jié)構(gòu)與訓(xùn)練方法,提出基于深度卷積神經(jīng)網(wǎng)絡(luò)的刀具狀態(tài)監(jiān)測(cè)方法;并且搭建卷積神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)平臺(tái),比較卷積神經(jīng)網(wǎng)絡(luò)不同模型的執(zhí)行效果,同時(shí)與傳統(tǒng)神經(jīng)網(wǎng)絡(luò)的預(yù)測(cè)性能進(jìn)行對(duì)比,驗(yàn)證所建立的模型有效性。(5)智慧車(chē)間加工作業(yè)的主動(dòng)調(diào)度給出調(diào)度模型的分類(lèi),構(gòu)建智慧車(chē)間加工作業(yè)的感知環(huán)境;提出一種主動(dòng)調(diào)度方案,具體研究包括加工作業(yè)調(diào)度數(shù)學(xué)模型、主動(dòng)調(diào)度框架、策略和多目標(biāo)雙層編碼雙級(jí)進(jìn)化雙重解碼遺傳算法(MD3GA);搭建智慧車(chē)間加工作業(yè)的原型平臺(tái),實(shí)現(xiàn)加工機(jī)器與AGV的集成調(diào)度,用實(shí)驗(yàn)加以驗(yàn)證所提出的主動(dòng)調(diào)度方法。
[Abstract]:With the development of cloud computing, Internet of things, big data, information physics fusion system, enterprise 2.0, industry 4.0 and so on, the deep integration of information technology and advanced manufacturing technology gives birth to intelligent manufacturing based on social information physics system. A service-oriented and knowledge-based collaborative manufacturing model for human-machine is formed. In the intelligent manufacturing environment, the Internet of things covers the whole workshop, and all kinds of sensors (such as RFID-accelerometers) deployed in the workshop monitor the whole production process in real time, and transmit the data to the processing center through the network. Due to various uncertain factors, the production process is prone to abnormal events, resulting in the production process information complex and difficult to control. It is necessary to process all kinds of sensor data in real time, mine out the abnormal events in the production site and predict the abnormal situation that will happen, and then realize the active scheduling of the production workshop equipment based on the real-time and the predicted abnormal events. Avoid damage to production system due to abnormal events. Therefore, this paper studies the active scheduling of workpiece abnormal event monitoring and tool residual life prediction based on machining. The main contents are as follows: (1) the characteristics of intelligent equipment are analyzed and summarized in the new intelligent manufacturing mode. This paper discusses the intelligent manufacturing model from the perspective of network fusion and social information physics system, and studies the social environment and key common technical problems to realize intelligent manufacturing. (2) the perceptual environment of intelligent manufacturing workshop is constructed based on the abnormal event monitoring of workpiece based on RFID. Various RFID event models are defined, including tag events, simple events and complex events, the framework of complex event processing system is given, and a comprehensive RFID data cleaning method is proposed to realize real-time workpiece anomaly event monitoring. Finally, the validity of data cleaning method and abnormal event monitoring is verified. (3) based on wireless accelerometer tool condition monitoring, the framework of tool condition monitoring system is given, and the experimental device of tool condition monitoring is built. Wavelet transform is used to remove vibration signal noise, different methods are used to extract the signal features in time domain, frequency domain and time frequency domain, and the key features are selected according to Pearson correlation coefficient, and a neurofuzzy networks (NFN) prediction model is established. The man-machine interface program for tool wear and residual life prediction is written and compared with backpropagation neural network and radial basis function network. (4) the structure and training methods of five depth learning models are compared, and the tool condition monitoring method based on deep convolution neural network is proposed, and the learning platform of convolution neural network is built. The performance of different models of convolution neural network is compared, and the prediction performance of traditional neural network is compared to verify the validity of the established model. (5) the active scheduling of intelligent job shop gives the classification of scheduling model. The perceptual environment of intelligent job shop is constructed, and an active scheduling scheme is proposed, including the mathematical model of processing job scheduling and the active scheduling framework. The strategy and multi-objective double-level evolutionary double decode genetic algorithm (MD3GA) are used to build the prototype platform of intelligent workshop to realize the integrated scheduling of machining machine and AGV. The proposed active scheduling method is verified by experiments.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TH186

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本文編號(hào)：2097251