基于異類傳感器融合的數(shù)控機(jī)床伺服系統(tǒng)故障診斷關(guān)鍵技術(shù)研究
發(fā)布時(shí)間：2018-09-06 09:11
【摘要】：數(shù)控機(jī)床加工精度高、質(zhì)量穩(wěn)定、加工速度快、生產(chǎn)效率高,是實(shí)現(xiàn)制造技術(shù)和裝備現(xiàn)代化的基石。伺服系統(tǒng)是數(shù)控機(jī)床及眾多復(fù)雜數(shù)控設(shè)備的關(guān)鍵部分,其性能好壞直接決定了整個(gè)設(shè)備的精度、工作效率及可靠性等。隨著數(shù)控機(jī)床的不斷發(fā)展,朝著結(jié)構(gòu)復(fù)雜化和高度自動化方向發(fā)展的數(shù)控機(jī)床伺服系統(tǒng),各部分之間的關(guān)聯(lián)更加密切,微小的故障往往會爆發(fā)連鎖反應(yīng),嚴(yán)重時(shí)會導(dǎo)致整個(gè)機(jī)床的性能異變、縮短壽命甚至報(bào)廢,后果危害性極大。機(jī)械設(shè)備的預(yù)測維修理念核心是通過對機(jī)械設(shè)備運(yùn)行過程的工況監(jiān)測、正確估計(jì)故障發(fā)展趨勢和演變規(guī)律,找出故障原因及時(shí)采取措施進(jìn)行維修保養(yǎng),達(dá)到“維修出效益”到“預(yù)測出效益”的理念變革。數(shù)控機(jī)床伺服系統(tǒng)與國防軍工、航空航天行業(yè)中的重型設(shè)備的伺服系統(tǒng)具有相同特征,研究成果相互通用。因此,開展數(shù)控機(jī)床伺服系統(tǒng)故障診斷的基礎(chǔ)理論與基本方法研究,對于提高我國數(shù)控機(jī)床在監(jiān)測、診斷、維護(hù)等方面的科技水平,十分必要。本文緊緊圍繞異類傳感器融合“采集什么信息”、“如何采集信息”、“如何利用信息”的三個(gè)關(guān)鍵技術(shù),結(jié)合目前數(shù)控機(jī)床伺服系統(tǒng)故障診斷研究存在的問題,即研究對象多為單一零部件、對內(nèi)置傳感器的價(jià)值挖掘不足、一直被作為故障分類和模式識別的問題來研究,層層推進(jìn)、步步深入,逐一展開了重點(diǎn)研究工作。以數(shù)學(xué)建模手段建立了整個(gè)伺服系統(tǒng)的復(fù)雜數(shù)學(xué)模型并進(jìn)行了穩(wěn)定性判別,理論分析了伺服系統(tǒng)各種典型故障機(jī)理與表現(xiàn),建立了故障表現(xiàn)與內(nèi)部參數(shù)之間的映射關(guān)系,并通過仿真進(jìn)行了驗(yàn)證,為利用內(nèi)置傳感器獲取機(jī)床本體信息的可靠性提供理論依據(jù)。以此為基礎(chǔ),結(jié)合外置傳感器檢測某一部件的傳統(tǒng)方法,提出了利用內(nèi)外置交叉互補(bǔ)的異類傳感器融合的新方法。搭建了異類傳感器融合的試驗(yàn)系統(tǒng),研究了利用內(nèi)外置傳感器獲取伺服系統(tǒng)本體信息的關(guān)鍵技術(shù)即數(shù)據(jù)對準(zhǔn)技術(shù),結(jié)合現(xiàn)有的試驗(yàn)基礎(chǔ)條件,提出了一種適用于802DSL數(shù)控系統(tǒng)和NI數(shù)據(jù)采集系統(tǒng)同步采集的時(shí)間對準(zhǔn)方案。與單單利用外置傳感器采集典型故障信息或者單單利用內(nèi)置傳感器獲取本體信息相比,豐富了信源、拓寬了信道。針對試驗(yàn)發(fā)現(xiàn)的通過外置傳感器檢測滾動軸承故障信號頻率與利用故障特征頻率計(jì)算公式計(jì)算的故障特征頻率存在著試驗(yàn)誤差及間諧波倍頻誤差這一問題,重點(diǎn)研究了誤差產(chǎn)生機(jī)理和積累與傳遞過程。通過對誤差改善和提高頻率分辨率各種技術(shù)手段的研究,得出基于特征頻率計(jì)算的滾動軸承故障診斷方法本身具有不可消除的模糊性。接著,提出一種基于數(shù)據(jù)驅(qū)動的滾動軸承故障診斷新方法。然后,對該方法模糊證據(jù)獲取得到的不確定度概率參數(shù)的理論分析和物理意義的挖掘,在故障診斷領(lǐng)域引入了直覺模糊集的概念,提出了將隨機(jī)集框架下的模糊證據(jù)獲取與匹配轉(zhuǎn)變?yōu)橹庇X模糊證據(jù)獲取及多元決策融合的新思路,并進(jìn)行了理論分析和試驗(yàn)研究。研究證明:一直作為故障分類和模式識別問題研究的多源信息融合的故障診斷,也可以看作多元決策融合問題。建立了基于直覺模糊決策加權(quán)融合的數(shù)控機(jī)床伺服系統(tǒng)故障分級診斷模型。首先,研究了時(shí)域、頻域及小波包降噪與EMD分解相結(jié)合的多域特征參數(shù)提取方法和基于極值間距的特征篩選及特征相關(guān)分析的數(shù)據(jù)降維。然后,構(gòu)建了基于遺傳BP網(wǎng)絡(luò)、RBF網(wǎng)絡(luò)與SVM的多分類器分級故障識別模型,并對三種智能識別模型的診斷能力進(jìn)行了比較分析,提出了利用單一分類器模型的診斷準(zhǔn)確率作為權(quán)重系數(shù),構(gòu)建基于加權(quán)集結(jié)算子的直覺模糊決策融合的數(shù)控機(jī)床伺服系統(tǒng)智能分級診斷模型,試驗(yàn)證明該方法對不同分類器之間存在分歧的樣本識別能力強(qiáng)、準(zhǔn)確率高,體現(xiàn)了方法本身的容錯(cuò)和自糾正能力。
[Abstract]:NC machine tools are the cornerstone of the modernization of manufacturing technology and equipment with high precision, stable quality, fast processing speed and high production efficiency. Servo system is the key part of NC machine tools and many complex NC equipment. Its performance directly determines the accuracy, efficiency and reliability of the whole equipment. With the development of the CNC machine tool servo system, which is developing toward the direction of complex structure and high automation, the relationship between the parts is more close. Small faults often break out chain reaction, which will lead to the performance variation of the whole machine tool, shorten the life and even scrap. The consequences are very harmful. By monitoring the operating conditions of mechanical equipment, correctly estimating the development trend and evolution law of the fault, finding out the causes of the fault and taking timely measures for maintenance, the concept transformation from "repairing benefit" to "predicting benefit" can be achieved. The servo system has the same characteristics, and the research results are common to each other. Therefore, it is necessary to study the basic theory and method of fault diagnosis for CNC machine tool servo system to improve the scientific and technological level of monitoring, diagnosis and maintenance of CNC machine tools in China. The three key technologies of "how to collect information" and "how to use information" are combined with the existing problems in fault diagnosis research of CNC machine tool servo system, that is, the research object is mostly a single component, and the value of the built-in sensor is not excavated enough, which has been studied as a problem of fault classification and pattern recognition. The complex mathematical model of the whole servo system is established by means of mathematical modeling and its stability is discriminated. The typical fault mechanism and performance of the servo system are analyzed theoretically. The mapping relationship between fault performance and internal parameters is established and verified by simulation. Based on the reliability of the built-in sensor to obtain the ontology information of the machine tool, combining with the traditional method of detecting a part with the external sensor, a new method of fusion of heterogeneous sensors with the internal and external cross-complementary is proposed. The key technology of servo system ontology information is data alignment technology. Combining with the existing experimental basic conditions, a time alignment scheme for synchronous acquisition of 802DSL CNC system and NI data acquisition system is proposed. It can collect typical fault information with external sensors or acquire ontology information with internal sensors. In order to solve the problem that there are test error and inter-harmonic frequency doubling error between the fault characteristic frequency calculated by the formula of fault characteristic frequency and the fault signal frequency detected by the external sensor, the mechanism of error generation and the process of accumulation and transmission are emphatically studied. Through the study of various technical means to improve the error and enhance the frequency resolution, it is concluded that the fault diagnosis method of rolling bearing based on the calculation of characteristic frequency has its own indelible fuzziness. Then, a new method of fault diagnosis of rolling bearing based on data-driven is proposed. The concept of intuitionistic fuzzy sets is introduced in the field of fault diagnosis. A new idea is proposed to transform the fuzzy evidence acquisition and matching under the framework of random sets into intuitionistic fuzzy evidence acquisition and multi-decision fusion. The theoretical analysis and experimental research are carried out. As a problem of fault classification and pattern recognition, multi-source information fusion can also be regarded as a problem of multi-decision fusion. A hierarchical fault diagnosis model of CNC machine tool servo system based on intuitionistic fuzzy decision weighted fusion is established. Firstly, the multi-domain features of time domain, frequency domain and wavelet packet denoising combined with EMD decomposition are studied. Feature parameter extraction method and data dimension reduction based on extremum distance feature selection and feature correlation analysis. Then, a multi-classifier hierarchical fault identification model based on genetic BP network, RBF network and SVM is constructed, and the diagnostic ability of the three intelligent recognition models is compared and analyzed, and the diagnosis based on single classifier model is proposed. Accuracy is taken as weight coefficient, and an intelligent hierarchical diagnosis model of CNC machine tool servo system based on intuitionistic fuzzy decision fusion with weighted aggregator is constructed. The experiment proves that the method has strong ability of identifying samples with different classifiers and high accuracy, which reflects the fault tolerance and self-correction ability of the method itself.
【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
本文編號：2225874
[Abstract]:NC machine tools are the cornerstone of the modernization of manufacturing technology and equipment with high precision, stable quality, fast processing speed and high production efficiency. Servo system is the key part of NC machine tools and many complex NC equipment. Its performance directly determines the accuracy, efficiency and reliability of the whole equipment. With the development of the CNC machine tool servo system, which is developing toward the direction of complex structure and high automation, the relationship between the parts is more close. Small faults often break out chain reaction, which will lead to the performance variation of the whole machine tool, shorten the life and even scrap. The consequences are very harmful. By monitoring the operating conditions of mechanical equipment, correctly estimating the development trend and evolution law of the fault, finding out the causes of the fault and taking timely measures for maintenance, the concept transformation from "repairing benefit" to "predicting benefit" can be achieved. The servo system has the same characteristics, and the research results are common to each other. Therefore, it is necessary to study the basic theory and method of fault diagnosis for CNC machine tool servo system to improve the scientific and technological level of monitoring, diagnosis and maintenance of CNC machine tools in China. The three key technologies of "how to collect information" and "how to use information" are combined with the existing problems in fault diagnosis research of CNC machine tool servo system, that is, the research object is mostly a single component, and the value of the built-in sensor is not excavated enough, which has been studied as a problem of fault classification and pattern recognition. The complex mathematical model of the whole servo system is established by means of mathematical modeling and its stability is discriminated. The typical fault mechanism and performance of the servo system are analyzed theoretically. The mapping relationship between fault performance and internal parameters is established and verified by simulation. Based on the reliability of the built-in sensor to obtain the ontology information of the machine tool, combining with the traditional method of detecting a part with the external sensor, a new method of fusion of heterogeneous sensors with the internal and external cross-complementary is proposed. The key technology of servo system ontology information is data alignment technology. Combining with the existing experimental basic conditions, a time alignment scheme for synchronous acquisition of 802DSL CNC system and NI data acquisition system is proposed. It can collect typical fault information with external sensors or acquire ontology information with internal sensors. In order to solve the problem that there are test error and inter-harmonic frequency doubling error between the fault characteristic frequency calculated by the formula of fault characteristic frequency and the fault signal frequency detected by the external sensor, the mechanism of error generation and the process of accumulation and transmission are emphatically studied. Through the study of various technical means to improve the error and enhance the frequency resolution, it is concluded that the fault diagnosis method of rolling bearing based on the calculation of characteristic frequency has its own indelible fuzziness. Then, a new method of fault diagnosis of rolling bearing based on data-driven is proposed. The concept of intuitionistic fuzzy sets is introduced in the field of fault diagnosis. A new idea is proposed to transform the fuzzy evidence acquisition and matching under the framework of random sets into intuitionistic fuzzy evidence acquisition and multi-decision fusion. The theoretical analysis and experimental research are carried out. As a problem of fault classification and pattern recognition, multi-source information fusion can also be regarded as a problem of multi-decision fusion. A hierarchical fault diagnosis model of CNC machine tool servo system based on intuitionistic fuzzy decision weighted fusion is established. Firstly, the multi-domain features of time domain, frequency domain and wavelet packet denoising combined with EMD decomposition are studied. Feature parameter extraction method and data dimension reduction based on extremum distance feature selection and feature correlation analysis. Then, a multi-classifier hierarchical fault identification model based on genetic BP network, RBF network and SVM is constructed, and the diagnostic ability of the three intelligent recognition models is compared and analyzed, and the diagnosis based on single classifier model is proposed. Accuracy is taken as weight coefficient, and an intelligent hierarchical diagnosis model of CNC machine tool servo system based on intuitionistic fuzzy decision fusion with weighted aggregator is constructed. The experiment proves that the method has strong ability of identifying samples with different classifiers and high accuracy, which reflects the fault tolerance and self-correction ability of the method itself.
【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
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 朱亞培;龍祖強(qiáng);劉燦;;傅里葉變換在數(shù)字圖像處理中的教學(xué)方法探討[J];輕工科技;2016年02期
2 牛彥杰;于愛榮;王智學(xué);張婷婷;;基于直覺模糊集的指揮控制群決策方法[J];指揮與控制學(xué)報(bào);2015年04期
3 戴浩;;指揮控制的理論創(chuàng)新——網(wǎng)絡(luò)賦能的C2[J];指揮與控制學(xué)報(bào);2015年01期
4 張龍;張磊;熊國良;黃文藝;周繼惠;;基于多尺度熵和神經(jīng)網(wǎng)絡(luò)的滾動軸承故障診斷[J];機(jī)械設(shè)計(jì)與研究;2014年05期
5 裴峻峰;畢昆磊;呂苗榮;賀超;沈科君;;基于多特征參數(shù)和概率神經(jīng)網(wǎng)絡(luò)的滾動軸承故障診斷方法[J];中國機(jī)械工程;2014年15期
6 韓德強(qiáng);楊藝;韓崇昭;;DS證據(jù)理論研究進(jìn)展及相關(guān)問題探討[J];控制與決策;2014年01期
7 于樹海;王建立;董磊;劉欣悅;王國聰;;非均勻采樣的傅里葉望遠(yuǎn)鏡數(shù)值模擬研究[J];光學(xué)學(xué)報(bào);2013年08期
8 向志軍;;ZOOMFFT頻譜細(xì)化分析方法在聲反饋抑制中的應(yīng)用[J];電聲技術(shù);2013年07期
9 孫鵬;;滾珠絲杠螺母副的故障診斷與維修[J];裝備制造技術(shù);2012年12期
10 王鵬飛;李暢;;不確定多屬性決策雙目標(biāo)組合賦權(quán)模型研究[J];中國管理科學(xué);2012年04期
相關(guān)博士學(xué)位論文 前4條
1 蘇文勝;滾動軸承振動信號處理及特征提取方法研究[D];大連理工大學(xué);2010年
2 楊建文;周期平穩(wěn)類機(jī)械故障信號分析方法研究[D];東南大學(xué);2006年
3 李宏坤;基于信息融合技術(shù)船舶柴油機(jī)故障診斷方法的研究與應(yīng)用[D];大連理工大學(xué);2003年
4 李書明;制造設(shè)備智能診斷與狀態(tài)預(yù)測技術(shù)的研究[D];天津大學(xué);1998年
相關(guān)碩士學(xué)位論文 前10條
1 溫國強(qiáng);基于多信息源的滾動軸承故障診斷方法與實(shí)驗(yàn)研究[D];青島理工大學(xué);2013年
2 宋平;數(shù)控機(jī)床工作臺進(jìn)給系統(tǒng)故障診斷研究[D];青島理工大學(xué);2013年
3 王歆峪;基于神經(jīng)網(wǎng)絡(luò)的電機(jī)故障診斷[D];上海交通大學(xué);2013年
4 王剛;高速精密滾珠絲杠副綜合性能測試系統(tǒng)開發(fā)[D];大連理工大學(xué);2012年
5 胡桐;數(shù)控機(jī)床進(jìn)給系統(tǒng)能量特性研究[D];重慶大學(xué);2012年
6 楊慧斌;滾動軸承故障診斷中的特征提取與選擇方法[D];湖南工業(yè)大學(xué);2011年
7 吳希曦;高檔數(shù)控機(jī)床關(guān)鍵部件故障智能診斷技術(shù)研究[D];西南交通大學(xué);2011年
8 林選;基于小波包和EMD相結(jié)合的電機(jī)軸承故障診斷[D];太原理工大學(xué);2010年
9 陳少陽;框架結(jié)構(gòu)基于神經(jīng)網(wǎng)絡(luò)的多級結(jié)構(gòu)損傷檢測方法研究[D];重慶大學(xué);2010年
10 向陽輝;基于信息融合技術(shù)的旋轉(zhuǎn)機(jī)械故障診斷研究[D];中南大學(xué);2007年
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