數(shù)控機(jī)床主軸組件故障的知識(shí)發(fā)現(xiàn)研究
發(fā)布時(shí)間：2018-08-30 15:23
【摘要】：數(shù)控機(jī)床故障診斷及維護(hù)是機(jī)床調(diào)試和使用過程中的重要組成部分,是目前制約數(shù)控機(jī)床發(fā)揮正常作用的主要因素之一�，F(xiàn)有數(shù)控機(jī)床故障自診斷系統(tǒng)能夠診斷常見電器系統(tǒng)故障及簡單的與系統(tǒng)相連部件的故障,但故障出現(xiàn)率較高且引起機(jī)床加工質(zhì)量下降的機(jī)械類故障的自診斷基本上還是盲點(diǎn),而主軸組件的故障在此類故障中占了相當(dāng)比重,這也一直是國內(nèi)外數(shù)控機(jī)床故障診斷領(lǐng)域的難題。 本文針對性提出從軟計(jì)算理論的全新視角解決該問題,對在獲取故障知識(shí)的數(shù)據(jù)準(zhǔn)備階段和知識(shí)發(fā)現(xiàn)階段的幾個(gè)關(guān)鍵問題展開了較為深入的研究和探索。 在知識(shí)獲取的數(shù)據(jù)準(zhǔn)備階段,進(jìn)行了兩個(gè)方面的研究工作。 首先選取數(shù)控機(jī)床主軸系統(tǒng)的兩大組件即滾動(dòng)軸承和齒輪作為研究對象,通過對比分析它們與一般機(jī)械振動(dòng)的機(jī)理后得到結(jié)論,即滾動(dòng)軸承故障主要表現(xiàn)為表面磨損和剝落,而主軸齒輪最主要的故障來源于運(yùn)動(dòng)中產(chǎn)生的齒面均勻磨損和局部剝落故障。對前者,在進(jìn)行知識(shí)獲取過程提取特征時(shí)可以用基頻及其整數(shù)或分?jǐn)?shù)倍頻處幅值為特征參數(shù)；對后者,可以依據(jù)振動(dòng)信號(hào)嚙合頻率及其兩側(cè)產(chǎn)生的邊頻帶的組合頻譜診斷故障。針對主軸齒輪故障數(shù)據(jù)獲取時(shí)的測點(diǎn)布置優(yōu)化問題,采用有限元建模分析和諧響應(yīng)分析,確定出主軸箱振動(dòng)測點(diǎn)的理論最佳位置。搭建了以上兩種組件故障模擬實(shí)驗(yàn)系統(tǒng),為后續(xù)研究工作獲取原始數(shù)據(jù)做了準(zhǔn)備。 其次,從數(shù)據(jù)采集和處理角度,特別提出使用一個(gè)三階低通巴特沃斯濾波器和一個(gè)三階高通巴特沃斯濾波器建立的帶通濾波器進(jìn)行濾波,并對該濾波過程進(jìn)行了數(shù)學(xué)分析；為了實(shí)現(xiàn)將傳感器獲取數(shù)據(jù)的融合,針對單一傳感器數(shù)據(jù)融合的時(shí)問性問題,提出了結(jié)合算術(shù)均值與遞推估計(jì)的數(shù)據(jù)融合方法,獲得了比算術(shù)平均值更可靠的測量結(jié)果,而針對多傳感器數(shù)據(jù)融合的空間性問題,提出一種多傳感器數(shù)據(jù)的加權(quán)融合算法,不同的傳感器按照相應(yīng)的權(quán)數(shù),在總均方誤差最小這一最優(yōu)條件下,根據(jù)各個(gè)傳感器所得到的測量值以自適應(yīng)的方式尋找其對應(yīng)的權(quán)數(shù),使融合后的數(shù)據(jù)結(jié)果達(dá)到最優(yōu),并提出采用信息熵來評價(jià)數(shù)據(jù)融合的效果。 在故障數(shù)據(jù)的知識(shí)發(fā)現(xiàn)過程階段,分別對兩種組件的故障采取不同軟計(jì)算方法獲取了故障知識(shí)規(guī)則,實(shí)現(xiàn)了故障診斷。 針對滾動(dòng)軸承故障實(shí)驗(yàn)所獲取數(shù)據(jù),分別運(yùn)用基于等間距聚類與屬性重要度約簡算法和基于k-均值聚類與區(qū)分矩陣約簡算法,均實(shí)現(xiàn)表面磨損和剝落故障及正常狀態(tài)三種模式的知識(shí)及規(guī)則的獲取。 針對數(shù)控機(jī)床主軸齒輪的典型故障診斷,構(gòu)建了一種具有三層網(wǎng)絡(luò)結(jié)構(gòu)模型的BP神經(jīng)網(wǎng)絡(luò),經(jīng)過實(shí)驗(yàn)數(shù)據(jù)樣本的訓(xùn)練和仿真,實(shí)例結(jié)果驗(yàn)證了該方法可以實(shí)現(xiàn)對齒輪齒面均勻磨損故障、齒面局部剝落故障以及正常狀態(tài)的識(shí)別。
[Abstract]:The fault diagnosis and maintenance of NC machine tool is an important part in the process of debugging and using, and it is one of the main factors restricting the normal function of NC machine tool at present. The existing CNC machine tool fault self-diagnosis system can diagnose the common electrical system faults and simple faults connected with the system, but the self-diagnosis of mechanical faults, which have a high occurrence rate and cause the machine tool machining quality to decline, is basically a blind spot. The malfunction of spindle assembly occupies a considerable proportion in this kind of fault, which has always been a difficult problem in the field of fault diagnosis of CNC machine tools at home and abroad. In this paper, a new perspective of soft computing theory is proposed to solve this problem, and some key problems in the stage of data preparation and knowledge discovery of fault knowledge acquisition are studied and explored deeply. In the data preparation stage of knowledge acquisition, two aspects of research work are carried out. Firstly, two main components of the spindle system of CNC machine tools, that is, rolling bearings and gears, are selected as the research objects. By comparing them with the mechanism of general mechanical vibration, the conclusion is drawn that the fault of rolling bearings is mainly manifested by surface wear and spalling. The main fault of spindle gear is caused by uniform wear and local spalling. For the former, the fundamental frequency, its integer or fractional frequency amplitude can be used as the feature parameter in the process of knowledge acquisition, and the fault can be diagnosed according to the meshing frequency of the vibration signal and the combined frequency spectrum of the edge band generated by both sides of the vibration signal. Aiming at the optimization of measuring point arrangement when the fault data of spindle gear is acquired, the theoretical optimum position of vibration measuring point of spindle box is determined by using finite element modeling and harmonious response analysis. The above two component fault simulation experiment systems are built to prepare for the subsequent research work to obtain the original data. Secondly, from the point of view of data acquisition and processing, a band-pass filter based on a third-order low-Tombatworth filter and a third-order high-Tunbartworth filter is proposed to filter, and the process of the filter is analyzed mathematically. In order to achieve the fusion of sensor data acquisition, a data fusion method combining arithmetic mean and recursive estimation is proposed to solve the temporal problem of single sensor data fusion. The measurement results are more reliable than arithmetic average. Aiming at the spatial problem of multi-sensor data fusion, a weighted fusion algorithm for multi-sensor data is proposed. According to the corresponding weights, different sensors are optimized under the optimal condition of minimum total mean square error. According to the measured values obtained from each sensor, the corresponding weights are found in an adaptive manner, so that the results of the fused data are optimized, and the information entropy is proposed to evaluate the effect of the data fusion. In the process of knowledge discovery of fault data, different soft computing methods are used to obtain fault knowledge rules and fault diagnosis is realized. In view of the data obtained from rolling bearing fault experiment, the algorithm based on equidistant clustering and attribute importance reduction and the algorithm based on k-means clustering and discernibility matrix reduction are used, respectively. The knowledge and rules of surface wear and peeling fault and normal state are obtained. Aiming at the typical fault diagnosis of spindle gear of NC machine tool, a BP neural network with three-layer network structure model is constructed, which is trained and simulated by experimental data sample. The results show that the method can recognize the uniform wear fault, the local spalling fault and the normal state of gear tooth surface.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：TG659;TH165.3
[Abstract]:The fault diagnosis and maintenance of NC machine tool is an important part in the process of debugging and using, and it is one of the main factors restricting the normal function of NC machine tool at present. The existing CNC machine tool fault self-diagnosis system can diagnose the common electrical system faults and simple faults connected with the system, but the self-diagnosis of mechanical faults, which have a high occurrence rate and cause the machine tool machining quality to decline, is basically a blind spot. The malfunction of spindle assembly occupies a considerable proportion in this kind of fault, which has always been a difficult problem in the field of fault diagnosis of CNC machine tools at home and abroad. In this paper, a new perspective of soft computing theory is proposed to solve this problem, and some key problems in the stage of data preparation and knowledge discovery of fault knowledge acquisition are studied and explored deeply. In the data preparation stage of knowledge acquisition, two aspects of research work are carried out. Firstly, two main components of the spindle system of CNC machine tools, that is, rolling bearings and gears, are selected as the research objects. By comparing them with the mechanism of general mechanical vibration, the conclusion is drawn that the fault of rolling bearings is mainly manifested by surface wear and spalling. The main fault of spindle gear is caused by uniform wear and local spalling. For the former, the fundamental frequency, its integer or fractional frequency amplitude can be used as the feature parameter in the process of knowledge acquisition, and the fault can be diagnosed according to the meshing frequency of the vibration signal and the combined frequency spectrum of the edge band generated by both sides of the vibration signal. Aiming at the optimization of measuring point arrangement when the fault data of spindle gear is acquired, the theoretical optimum position of vibration measuring point of spindle box is determined by using finite element modeling and harmonious response analysis. The above two component fault simulation experiment systems are built to prepare for the subsequent research work to obtain the original data. Secondly, from the point of view of data acquisition and processing, a band-pass filter based on a third-order low-Tombatworth filter and a third-order high-Tunbartworth filter is proposed to filter, and the process of the filter is analyzed mathematically. In order to achieve the fusion of sensor data acquisition, a data fusion method combining arithmetic mean and recursive estimation is proposed to solve the temporal problem of single sensor data fusion. The measurement results are more reliable than arithmetic average. Aiming at the spatial problem of multi-sensor data fusion, a weighted fusion algorithm for multi-sensor data is proposed. According to the corresponding weights, different sensors are optimized under the optimal condition of minimum total mean square error. According to the measured values obtained from each sensor, the corresponding weights are found in an adaptive manner, so that the results of the fused data are optimized, and the information entropy is proposed to evaluate the effect of the data fusion. In the process of knowledge discovery of fault data, different soft computing methods are used to obtain fault knowledge rules and fault diagnosis is realized. In view of the data obtained from rolling bearing fault experiment, the algorithm based on equidistant clustering and attribute importance reduction and the algorithm based on k-means clustering and discernibility matrix reduction are used, respectively. The knowledge and rules of surface wear and peeling fault and normal state are obtained. Aiming at the typical fault diagnosis of spindle gear of NC machine tool, a BP neural network with three-layer network structure model is constructed, which is trained and simulated by experimental data sample. The results show that the method can recognize the uniform wear fault, the local spalling fault and the normal state of gear tooth surface.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：TG659;TH165.3
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