數(shù)控機床工作臺進給系統(tǒng)故障診斷研究
發(fā)布時間：2019-06-19 13:09
【摘要】：數(shù)控機床是現(xiàn)代工業(yè)生產(chǎn)的主力設備，特別是在加工結構復雜、大型和高精密零件時，數(shù)控機床發(fā)揮了不可替代的作用。但是數(shù)控機床通常處于高速、變載以及往復沖擊的工作環(huán)境下，，長時間的工作數(shù)控機床可能會產(chǎn)生故障，特別是一些機械部件如絲杠、軸承、導軌等。開展數(shù)控機床故障診斷研究可以及時的發(fā)現(xiàn)機床故障并找出故障隱患，從而提高機床的可靠性，并推動數(shù)控機床故障診斷技術由故障后維修和定期維修到實時維修的轉變，達到降低維修的成本，創(chuàng)造更大經(jīng)濟效益的目的。 本文研究了數(shù)控機床的常見故障形式及其故障機理并基于BP神經(jīng)網(wǎng)絡設計了數(shù)控機床工作臺進給系統(tǒng)的故障診斷系統(tǒng)。主要包括故障類型及機理分析、實驗方案的設計、數(shù)據(jù)采集系統(tǒng)的軟硬件設計、信號分析與特征值提取和基于神經(jīng)網(wǎng)絡的故障診斷模型設計等內(nèi)容。重點研究了信號處理技術包括信號預處理技術、特征提取技術和特征選擇技術以及兩級故障診斷模型的設計和實現(xiàn)等。 首先，研究了數(shù)控機床的常見故障及其機理，對故障發(fā)生比較頻繁的機械部件進行了重點研究。并以此為根據(jù)設計了實驗方案，包括故障件的選擇和設置、測點的選擇，傳感器的選擇和安裝，以及具體實驗流程的設計等。 其次，研究了數(shù)據(jù)采集技術，并設計了數(shù)據(jù)采集系統(tǒng)，包括硬件系統(tǒng)設計和軟件系統(tǒng)設計兩大部分。硬件設計是在NI-PXI的基礎上選擇了數(shù)據(jù)采集平臺和數(shù)據(jù)采集卡以及相應的線纜和調(diào)理設備并對其參數(shù)進行了設定；軟件系統(tǒng)設計主要基于LabVIEW和MATLAB平臺設計了數(shù)據(jù)采集模塊、數(shù)據(jù)分析模塊和數(shù)據(jù)庫管理模塊三大模塊，并編制了程序。 再次，研究了數(shù)據(jù)處理技術，對本文所采集到的數(shù)據(jù)的處理共分為三大步。第一步，對采集到的數(shù)據(jù)進行信號預處理，包括去除奇異點處理和信號零均值處理；第二步，對經(jīng)過預處理的信號分別進行時域分析、頻域分析和小波分析，并提取相應的時頻特征值；第三步，對提取的時頻特征值進行進一步的選擇和提取，包括特征值初步選擇和基于核主元分析的特征提取兩部分，最終得到用于故障診斷的特征值。 最后，建立了基于BP神經(jīng)網(wǎng)絡的數(shù)控機床工作臺進給系統(tǒng)的兩級故障診斷模型。第一級為總網(wǎng)絡，用來診斷不同部件的故障；第二級為各個子網(wǎng)絡，用來診斷同一部件的不同故障，分為滾動軸承網(wǎng)絡和滾珠絲杠網(wǎng)絡兩個子網(wǎng)絡。兩級故障診斷模型實現(xiàn)了故障的初步判別和故障的細化診斷功能。
[Abstract]:CNC machine tool is the main equipment of modern industrial production, especially when the machining structure is complex, large and high precision parts, CNC machine tool plays an irreplaceable role. However, CNC machine tools are usually in the working environment of high speed, variable load and reciprocating impact, and long working CNC machine tools may have faults, especially some mechanical components such as screw, bearing, guideway and so on. The fault diagnosis research of NC machine tool can find out the fault of machine tool in time and find out the hidden trouble, so as to improve the reliability of machine tool, and promote the transformation of fault diagnosis technology of NC machine tool from post-fault maintenance and regular maintenance to real-time maintenance, so as to reduce the cost of maintenance and create greater economic benefits. In this paper, the common fault forms and fault mechanism of NC machine tools are studied, and the fault diagnosis system of NC machine tool table feed system is designed based on BP neural network. It mainly includes the analysis of fault type and mechanism, the design of experimental scheme, the design of software and hardware of data acquisition system, signal analysis and eigenvalue extraction, and the design of fault diagnosis model based on neural network. The signal processing technology, including signal preprocessing technology, feature extraction technology and feature selection technology, as well as the design and implementation of two-level fault diagnosis model, are studied in detail. Firstly, the common faults and their mechanisms of NC machine tools are studied, and the mechanical components with frequent faults are studied. According to this, the experimental scheme is designed, including the selection and setting of fault parts, the selection of measuring points, the selection and installation of sensors, and the design of specific experimental flow. Secondly, the data acquisition technology is studied, and the data acquisition system is designed, including hardware system design and software system design. On the basis of NI-PXI, the hardware design selects the data acquisition platform and data acquisition card, as well as the corresponding cable and conditioning equipment, and sets its parameters. The software system design mainly designs three modules based on LabVIEW and MATLAB platform: data acquisition module, data analysis module and database management module, and compiles the program. Thirdly, the data processing technology is studied, and the data processing collected in this paper is divided into three steps. In the first step, the collected data are preprocessed, including the removal of singular points and the zero-mean processing of the signal. In the second step, the preprocessed signals are analyzed in time domain, frequency domain and wavelet, and the corresponding time-frequency eigenvalues are extracted. In the third step, the extracted time-frequency eigenvalues are further selected and extracted, including the preliminary selection of eigenvalues and the feature extraction based on kernel principal component analysis, and finally the eigenvalues for fault diagnosis are obtained. Finally, a two-stage fault diagnosis model of NC machine tool table feed system based on BP neural network is established. The first level is the general network, which is used to diagnose the faults of different components, and the second level is each sub-network, which is used to diagnose the different faults of the same component, which is divided into two sub-networks: rolling bearing network and ball screw network. The two-stage fault diagnosis model realizes the functions of preliminary fault discrimination and fault refinement diagnosis.
【學位授予單位】：青島理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TG659;TH165.3
本文編號：2502365
[Abstract]:CNC machine tool is the main equipment of modern industrial production, especially when the machining structure is complex, large and high precision parts, CNC machine tool plays an irreplaceable role. However, CNC machine tools are usually in the working environment of high speed, variable load and reciprocating impact, and long working CNC machine tools may have faults, especially some mechanical components such as screw, bearing, guideway and so on. The fault diagnosis research of NC machine tool can find out the fault of machine tool in time and find out the hidden trouble, so as to improve the reliability of machine tool, and promote the transformation of fault diagnosis technology of NC machine tool from post-fault maintenance and regular maintenance to real-time maintenance, so as to reduce the cost of maintenance and create greater economic benefits. In this paper, the common fault forms and fault mechanism of NC machine tools are studied, and the fault diagnosis system of NC machine tool table feed system is designed based on BP neural network. It mainly includes the analysis of fault type and mechanism, the design of experimental scheme, the design of software and hardware of data acquisition system, signal analysis and eigenvalue extraction, and the design of fault diagnosis model based on neural network. The signal processing technology, including signal preprocessing technology, feature extraction technology and feature selection technology, as well as the design and implementation of two-level fault diagnosis model, are studied in detail. Firstly, the common faults and their mechanisms of NC machine tools are studied, and the mechanical components with frequent faults are studied. According to this, the experimental scheme is designed, including the selection and setting of fault parts, the selection of measuring points, the selection and installation of sensors, and the design of specific experimental flow. Secondly, the data acquisition technology is studied, and the data acquisition system is designed, including hardware system design and software system design. On the basis of NI-PXI, the hardware design selects the data acquisition platform and data acquisition card, as well as the corresponding cable and conditioning equipment, and sets its parameters. The software system design mainly designs three modules based on LabVIEW and MATLAB platform: data acquisition module, data analysis module and database management module, and compiles the program. Thirdly, the data processing technology is studied, and the data processing collected in this paper is divided into three steps. In the first step, the collected data are preprocessed, including the removal of singular points and the zero-mean processing of the signal. In the second step, the preprocessed signals are analyzed in time domain, frequency domain and wavelet, and the corresponding time-frequency eigenvalues are extracted. In the third step, the extracted time-frequency eigenvalues are further selected and extracted, including the preliminary selection of eigenvalues and the feature extraction based on kernel principal component analysis, and finally the eigenvalues for fault diagnosis are obtained. Finally, a two-stage fault diagnosis model of NC machine tool table feed system based on BP neural network is established. The first level is the general network, which is used to diagnose the faults of different components, and the second level is each sub-network, which is used to diagnose the different faults of the same component, which is divided into two sub-networks: rolling bearing network and ball screw network. The two-stage fault diagnosis model realizes the functions of preliminary fault discrimination and fault refinement diagnosis.
【學位授予單位】：青島理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TG659;TH165.3
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