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

【摘要】：隨著云計算、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,信息技術與先進制造技術深度融合,孕育出基于社會信息物理系統(tǒng)的智慧制造,形成一種面向服務、基于知識運用的人機物協(xié)同制造模式。在智慧制造環(huán)境下,物聯(lián)網(wǎng)覆蓋整個生產(chǎn)車間,部署于車間的各種傳感器(如RFID、加速度計等)實時監(jiān)測整個生產(chǎn)過程,并通過網(wǎng)絡將數(shù)據(jù)傳送到處理中心。由于各種不確定因素,導致生產(chǎn)過程容易發(fā)生異常事件,造成生產(chǎn)過程的信息復雜且不易控制。需要對各種傳感器數(shù)據(jù)實時處理,挖掘出生產(chǎn)現(xiàn)場的異常事件,并預測將要發(fā)生的異常狀況,進而基于實時與預測的異常事件,實現(xiàn)生產(chǎn)車間設備主動調(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,本論文研究基于機械加工的工件異常事件監(jiān)測和刀具剩余壽命預測的主動調(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智慧裝備的特征,探討網(wǎng)絡融合與社會信息物理系統(tǒng)視角下的智慧制造模式,研究實現(xiàn)智慧制造的社會環(huán)境與關鍵共性技術問題。(2)基于RFID的工件異常事件監(jiān)測構建智慧制造車間的感知環(huán)境,定義各類RFID事件模型,包括標簽事件、簡單事件和復雜事件等;給出復雜事件處理系統(tǒng)的框架,提出綜合的RFID數(shù)據(jù)清洗方法,實現(xiàn)面向?qū)崟r的工件異常事件監(jiān)測,最后實驗驗證數(shù)據(jù)清洗方法和異常事件監(jiān)測的有效性。(3)基于無線加速度計的刀具狀態(tài)監(jiān)測給出刀具狀態(tài)監(jiān)測系統(tǒng)的框架,并搭建刀具狀態(tài)監(jiān)測的實驗裝置;應用小波變換去除振動信號噪聲,用不同的方法提取信號在時域、頻域和時頻域的特征,并依據(jù)皮爾森相關系數(shù)選擇關鍵特征;建立神經(jīng)模糊網(wǎng)絡(Neuro-Fuzzy Networks,NFN)預測模型,編寫刀具磨損與剩余壽命預測的人機接口程序,并與反向傳播神經(jīng)網(wǎng)絡、徑向基函數(shù)網(wǎng)絡相比較,驗證NFN預測效果。(4)基于深度學習的刀具狀態(tài)監(jiān)測比較5種深度學習模型的結構與訓練方法,提出基于深度卷積神經(jīng)網(wǎng)絡的刀具狀態(tài)監(jiān)測方法;并且搭建卷積神經(jīng)網(wǎng)絡學習平臺,比較卷積神經(jīng)網(wǎng)絡不同模型的執(zhí)行效果,同時與傳統(tǒng)神經(jīng)網(wǎng)絡的預測性能進行對比,驗證所建立的模型有效性。(5)智慧車間加工作業(yè)的主動調(diào)度給出調(diào)度模型的分類,構建智慧車間加工作業(yè)的感知環(huán)境;提出一種主動調(diào)度方案,具體研究包括加工作業(yè)調(diào)度數(shù)學模型、主動調(diào)度框架、策略和多目標雙層編碼雙級進化雙重解碼遺傳算法(MD3GA);搭建智慧車間加工作業(yè)的原型平臺,實現(xiàn)加工機器與AGV的集成調(diào)度,用實驗加以驗證所提出的主動調(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.
【學位授予單位】：華南理工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TH186

【相似文獻】

相關期刊論文 前2條

1 姚谷;;在集裝箱加工中的新材料應用[J];中國科技信息;2013年13期

2 ;[J];;年期

相關博士學位論文 前1條

1 張存吉;智慧制造環(huán)境下感知數(shù)據(jù)驅(qū)動的加工作業(yè)主動調(diào)度方法研究[D];華南理工大學;2016年

相關碩士學位論文 前3條

1 周成;供給不確定情況下供應鏈加工作業(yè)再配置研究[D];東南大學;2016年

2 鄭龍;集成加工作業(yè)選擇與公差分配的智能優(yōu)化方法及應用[D];中國礦業(yè)大學;2014年

3 周國防;面向鋼鐵混合加工作業(yè)的資源分配方法研究[D];大連理工大學;2011年

，

本文編號：2097251