發(fā)布時間：2018-05-09 18:58

  本文選題：數(shù)控機床 + 故障診斷�。� 參考：《青島理工大學(xué)》2016年博士論文

【摘要】：數(shù)控機床是指采用數(shù)字控制系統(tǒng)的自動化機床,可實現(xiàn)自動換刀以及復(fù)雜曲線、曲面的加工,具有加工精度高、加工質(zhì)量穩(wěn)定、生產(chǎn)效率高的特點,因而成為現(xiàn)代制造生產(chǎn)中的關(guān)鍵技術(shù)設(shè)備,其技術(shù)發(fā)展水平與擁有數(shù)目成為體現(xiàn)一個國家工業(yè)現(xiàn)代化水平的重要標(biāo)志。從結(jié)構(gòu)組成來看,數(shù)控機床是集機械、電子、液壓等技術(shù)于一體的復(fù)雜系統(tǒng)。在使用過程中,任何一個部分出現(xiàn)故障,均會影響機床的正常運行,尤其機械部分出現(xiàn)故障時,長時間的停機檢修,導(dǎo)致整個生產(chǎn)線停產(chǎn),造成巨大的經(jīng)濟損失。目前數(shù)控機床機械部件仍然廣泛采用定期維護與定期更換的維修制度,這種維修制度下維修過度與維修不足的矛盾突出,一方面造成人力與物質(zhì)資源的極大浪費,另一方面無法避免數(shù)控機床突發(fā)性故障的發(fā)生。因此,開展數(shù)控機床狀態(tài)監(jiān)測與故障診斷研究,實現(xiàn)維護方式由定期更換到預(yù)防維護、預(yù)知維修的轉(zhuǎn)換是非常必要的。本文以信息融合技術(shù)為基礎(chǔ),對數(shù)控機床狀態(tài)監(jiān)測與故障診斷的策略、信號處理與特征提取的方法、故障模式智能識別模型的建立以及全局綜合決策融合方法等進行了深入地研究,設(shè)計了基于多層次信息融合的數(shù)控機床機械部件狀態(tài)監(jiān)測與故障診斷系統(tǒng)。論文從切削力分析入手,根據(jù)數(shù)控機床載荷多變、高頻沖擊的工況以及加工多樣性的特點,得出其切削力傳播路徑上的機械零部件更易發(fā)生故障的結(jié)論,分析了數(shù)控機床上機械零部件與普通設(shè)備故障成因的不同,確定了論文的研究對象以及故障失效形式。研究了基于振動、溫度、電機電流、伺服誤差等多種參量的故障診斷機理,并按照參量信息來源的不同,構(gòu)建了由外部傳感器、內(nèi)部信息、程序參數(shù)以及警報信息組成的數(shù)控機床多維感知狀態(tài)監(jiān)測體系,從機床本體、刀具磨損、加工過程、工件加工質(zhì)量等多個角度、多個方面反映數(shù)控機床運行狀態(tài),實現(xiàn)數(shù)控機床的全方面監(jiān)測,為后續(xù)診斷過程提供充足的信息。通過實驗數(shù)據(jù)分析發(fā)現(xiàn)僅用傳統(tǒng)的時域與頻譜分析不能對復(fù)合故障進行有效地區(qū)分,為進一步挖掘隱藏在原始信號中的故障特征,本文提出了小波包與經(jīng)驗?zāi)B(tài)分解聯(lián)合的信號處理方法,利用小波包對信號進行降噪,并將小波重構(gòu)信號擴展為高頻與低頻兩個窄帶信號,再分別對兩個窄帶信號進行EMD處理的方法。這種小波包與經(jīng)驗?zāi)B(tài)分解聯(lián)合的信號處理方法,利用小波包對信號進行降噪,大大提高EMD分解的精度與質(zhì)量,而且通過重構(gòu)節(jié)點的擴展,可以更加細(xì)致地分析故障信息。提取EMD分解后每個IMF的能量作為特征,與時域特征、頻域特征組成多域混合特征集合�；谔卣髦g相關(guān)性分析的特征選擇方法,以模糊聚類為主要手段進行特征降維,獲取敏感特征子集。根據(jù)數(shù)控機床需要診斷的對象及其故障多的特點,提出了分級診斷的策略,將診斷劃分為故障定位、故障類別與程度兩個層次。主網(wǎng)絡(luò)在對故障定位的同時,負(fù)責(zé)局部子網(wǎng)絡(luò)模型結(jié)果的聚合;局部子網(wǎng)絡(luò)診斷具體的故障類型與程度。通過任務(wù)分工與協(xié)作,達(dá)到了簡化網(wǎng)絡(luò)結(jié)構(gòu)的目的。研究了數(shù)控機床故障診斷的BP神經(jīng)網(wǎng)絡(luò)模型、RBF神經(jīng)網(wǎng)絡(luò)模型及支持向量機(SVM)模型的構(gòu)建依據(jù)和方法,以敏感特征作為模型輸入,分別構(gòu)建了基于BP、RBF與SVM的數(shù)控機床故障診斷主網(wǎng)絡(luò)與局部診斷子網(wǎng)絡(luò)的模型,對比研究了三種模型對不同故障類別的診斷能力。建立了基于模糊綜合評判的全局診斷模型與基于加權(quán)D-S證據(jù)理論的全局診斷模型,進一步地提高了故障識別率。首先針對數(shù)控機床模糊綜合評判建模的難點,提出了以多分類器的初步診斷結(jié)果為基礎(chǔ),將評價因素由高維特征轉(zhuǎn)變?yōu)榈途S的初級診斷結(jié)果,降低了模型的復(fù)雜程度,成功構(gòu)建了基于信息融合的數(shù)控機床單級模糊綜合評判故障診斷模型,并提出了從正確性與診斷精度兩個方面評價分類器分類能力的方法,構(gòu)造了基于信息熵的評價函數(shù)以及分類器整體平均的權(quán)重分配方法,減少了人為主觀因素的影響。構(gòu)造了分類能力評價矩陣,有效地解決了分類器對不同故障類型識別率差異較大時的權(quán)重分配問題。針對證據(jù)理論合成規(guī)則在處理高沖突證據(jù)時,得出結(jié)論與事實相悖的問題,提出了基于加權(quán)的證據(jù)理論診斷模型,以分類器故障識別率作為權(quán)重對原始證據(jù)進行加權(quán),有效地降低了證據(jù)沖突率,故障識別率得以提高。搭建了數(shù)控機床故障診斷實驗系統(tǒng),對本文所提出的模型與方法進行了實驗驗證。
[Abstract]:CNC machine tool is an automatic machine tool with digital control system, which can realize automatic knife exchange, complicated curve and surface processing. It has the characteristics of high machining precision, stable processing quality and high production efficiency. Therefore, it has become the key technology equipment in modern manufacturing and production, and its technical development level and the number of ownership become a country. The important symbol of the level of industrial modernization. From the structure composition, the CNC machine tool is a complex system which integrates mechanical, electronic, hydraulic and other technologies. In the process of use, any part of the machine can affect the normal operation of the machine tool, especially when the mechanical part occurs the obstacle, long time stop overhaul, causing the whole production line to stop. Production has caused huge economic losses. At present, the mechanical parts of CNC machine tools are still widely used for regular maintenance and regular replacement. The contradiction between excessive maintenance and insufficient maintenance is prominent under this maintenance system. On the one hand, it causes great waste of human and material resources, and on the other hand it can not avoid the occurrence of sudden failure of CNC machine tools. Therefore, the research of state monitoring and fault diagnosis of CNC machine tools is carried out, and the maintenance mode is changed from regular to preventive maintenance. The transformation of predictive maintenance is very necessary. Based on information fusion technology, the strategy of state monitoring and fault diagnosis of CNC machine tools, the method of signal processing and feature extraction, and intelligent identification of fault mode The establishment of the model and the fusion method of global comprehensive decision making are studied deeply. The system of state monitoring and fault diagnosis of the mechanical parts of CNC machine tools based on multilevel information fusion is designed. The paper starts with the analysis of the cutting force, and draws the characteristics of the variable load, the working condition of the high frequency punching and the diversity of the machining. The conclusion that the mechanical parts of the cutting force propagation path are more prone to failure is analyzed. The causes of the failure of the mechanical parts and the common equipment on the CNC machine tools are analyzed. The research objects and the failure modes of the paper are determined. The fault diagnosis mechanism based on the vibration, temperature, motor current and servo error is studied. According to the different sources of the parameter information, a multi-dimensional sensing state monitoring system of CNC machine tools, consisting of external sensors, internal information, program parameters and alarm information, has been constructed. From the machine tool body, tool wear, processing process, workpiece processing quality and many other angles, many sides reflect the running state of CNC machine tools and realize the whole CNC machine tools. It provides sufficient information for the follow-up diagnosis process. Through the analysis of experimental data, it is found that only the traditional time domain and spectrum analysis can not be effectively divided into the complex faults. In order to further excavate the fault features hidden in the original signal, this paper proposes a signal processing method combining the wavelet packet and the empirical mode decomposition. The wavelet packet is used to denoise the signal, and the wavelet reconstruction signal is extended to two narrow band signals of high frequency and low frequency, and then two narrow band signals are processed by EMD. The signal processing method combining the wavelet packet and the empirical mode decomposition is used to reduce the noise by the wavelet packet, which greatly improves the precision and quality of the EMD decomposition. In addition, the fault information can be analyzed more carefully by the expansion of the node. The energy of each IMF after EMD decomposition is extracted as the feature, and the multi domain mixed feature set is formed with the time domain features and frequency domain features. The feature selection method based on the correlation analysis between features is used as the main means to reduce the feature dimension and obtain the sensitivity. Feature subset. According to the characteristics that the CNC machine needs to diagnose and the characteristics of many faults, the strategy of grading diagnosis is put forward, and the diagnosis is divided into two levels of fault location, fault category and degree. The main network is responsible for the aggregation of the results of local subnetwork model while the fault location is located. Degree. Through task division and cooperation, the purpose of simplifying the network structure is achieved. The BP neural network model, the RBF neural network model and the support vector machine (SVM) model for CNC machine tool fault diagnosis are studied. The fault diagnosis of CNC machine tools based on BP, RBF and SVM is constructed by using the sensitive features as the model input. The model of the main network and the local diagnostic subnetwork is used to compare the diagnosis ability of the three models to different fault categories. The global diagnosis model based on fuzzy comprehensive evaluation and the global diagnosis model based on the weighted D-S evidence theory are established, and the fault recognition rate is further improved. First, the fuzzy comprehensive evaluation modeling of CNC machine tools is established. On the basis of the preliminary diagnosis results of multiple classifiers, the evaluation factors are transformed from high dimensional features to low dimension primary diagnosis results, and the complexity of the model is reduced. A single level fuzzy comprehensive evaluation model for numerical control machine tool based on information fusion is successfully constructed, and the correctness and diagnostic accuracy are two. To evaluate the classification ability of the classifier, the evaluation function based on information entropy and the weight allocation method of the overall average of the classifier are constructed, and the influence of the subjective factors is reduced. The classification ability evaluation matrix is constructed, which effectively solves the weight allocation problem when the classifier differs greatly from the recognition rate of different fault types. When the evidence theory synthesis rule is dealing with the high conflict evidence, the conclusion is contrary to the fact. The weighted evidence theory diagnosis model is put forward, which is weighted to the original evidence with the classifier fault recognition rate as the weight, thus effectively reducing the evidence conflict rate, so the recognition rate of the barrier is improved. The fault of the CNC machine tool is built up. The diagnostic experiment system is tested by the model and method proposed in this paper.

【學(xué)位授予單位】：青島理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TG659

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本文編號：1867034