發(fā)布時間：2018-09-06 09:11

【摘要】：數控機床加工精度高、質量穩(wěn)定、加工速度快、生產效率高,是實現制造技術和裝備現代化的基石。伺服系統(tǒng)是數控機床及眾多復雜數控設備的關鍵部分,其性能好壞直接決定了整個設備的精度、工作效率及可靠性等。隨著數控機床的不斷發(fā)展,朝著結構復雜化和高度自動化方向發(fā)展的數控機床伺服系統(tǒng),各部分之間的關聯更加密切,微小的故障往往會爆發(fā)連鎖反應,嚴重時會導致整個機床的性能異變、縮短壽命甚至報廢,后果危害性極大。機械設備的預測維修理念核心是通過對機械設備運行過程的工況監(jiān)測、正確估計故障發(fā)展趨勢和演變規(guī)律,找出故障原因及時采取措施進行維修保養(yǎng),達到“維修出效益”到“預測出效益”的理念變革。數控機床伺服系統(tǒng)與國防軍工、航空航天行業(yè)中的重型設備的伺服系統(tǒng)具有相同特征,研究成果相互通用。因此,開展數控機床伺服系統(tǒng)故障診斷的基礎理論與基本方法研究,對于提高我國數控機床在監(jiān)測、診斷、維護等方面的科技水平,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”的三個關鍵技術,結合目前數控機床伺服系統(tǒng)故障診斷研究存在的問題,即研究對象多為單一零部件、對內置傳感器的價值挖掘不足、一直被作為故障分類和模式識別的問題來研究,層層推進、步步深入,逐一展開了重點研究工作。以數學建模手段建立了整個伺服系統(tǒng)的復雜數學模型并進行了穩(wěn)定性判別,理論分析了伺服系統(tǒng)各種典型故障機理與表現,建立了故障表現與內部參數之間的映射關系,并通過仿真進行了驗證,為利用內置傳感器獲取機床本體信息的可靠性提供理論依據。以此為基礎,結合外置傳感器檢測某一部件的傳統(tǒng)方法,提出了利用內外置交叉互補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系統(tǒng),研究了利用內外置傳感器獲取伺服系統(tǒng)本體信息的關鍵技術即數據對準技術,結合現有的試驗基礎條件,提出了一種適用于802DSL數控系統(tǒng)和NI數據采集系統(tǒng)同步采集的時間對準方案。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。針對試驗發(fā)現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算公式計算的故障特征頻率存在著試驗誤差及間諧波倍頻誤差這一問題,重點研究了誤差產生機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手段的研究,得出基于特征頻率計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,提出一種基于數據驅動的滾動軸承故障診斷新方法。然后,對該方法模糊證據獲取得到的不確定度概率參數的理論分析和物理意義的挖掘,在故障診斷領域引入了直覺模糊集的概念,提出了將隨機集框架下的模糊證據獲取與匹配轉變?yōu)橹庇X模糊證據獲取及多元決策融合的新思路,并進行了理論分析和試驗研究。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融合的數控機床伺服系統(tǒng)故障分級診斷模型。首先,研究了時域、頻域及小波包降噪與EMD分解相結合的多域特征參數提取方法和基于極值間距的特征篩選及特征相關分析的數據降維。然后,構建了基于遺傳BP網絡、RBF網絡與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行了比較分析,提出了利用單一分類器模型的診斷準確率作為權重系數,構建基于加權集結算子的直覺模糊決策融合的數控機床伺服系統(tǒng)智能分級診斷模型,試驗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、準確率高,體現了方法本身的容錯和自糾正能力。
[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.
【學位授予單位】：青島理工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TG659

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