數(shù)控機(jī)床工作臺進(jìn)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(jī)床是現(xiàn)代工業(yè)生產(chǎn)的主力設(shè)備，特別是在加工結(jié)構(gòu)復(fù)雜、大型和高精密零件時，數(shù)控機(jī)床發(fā)揮了不可替代的作用。但是數(shù)控機(jī)床通常處于高速、變載以及往復(fù)沖擊的工作環(huán)境下，，長時間的工作數(shù)控機(jī)床可能會產(chǎn)生故障，特別是一些機(jī)械部件如絲杠、軸承、導(dǎo)軌等。開展數(shù)控機(jī)床故障診斷研究可以及時的發(fā)現(xiàn)機(jī)床故障并找出故障隱患，從而提高機(jī)床的可靠性，并推動數(shù)控機(jī)床故障診斷技術(shù)由故障后維修和定期維修到實時維修的轉(zhuǎn)變，達(dá)到降低維修的成本，創(chuàng)造更大經(jīng)濟(jì)效益的目的。 本文研究了數(shù)控機(jī)床的常見故障形式及其故障機(jī)理并基于BP神經(jīng)網(wǎng)絡(luò)設(shè)計了數(shù)控機(jī)床工作臺進(jìn)給系統(tǒng)的故障診斷系統(tǒng)。主要包括故障類型及機(jī)理分析、實驗方案的設(shè)計、數(shù)據(jù)采集系統(tǒng)的軟硬件設(shè)計、信號分析與特征值提取和基于神經(jīng)網(wǎng)絡(luò)的故障診斷模型設(shè)計等內(nèi)容。重點(diǎn)研究了信號處理技術(shù)包括信號預(yù)處理技術(shù)、特征提取技術(shù)和特征選擇技術(shù)以及兩級故障診斷模型的設(shè)計和實現(xiàn)等。 首先，研究了數(shù)控機(jī)床的常見故障及其機(jī)理，對故障發(fā)生比較頻繁的機(jī)械部件進(jìn)行了重點(diǎn)研究。并以此為根據(jù)設(shè)計了實驗方案，包括故障件的選擇和設(shè)置、測點(diǎn)的選擇，傳感器的選擇和安裝，以及具體實驗流程的設(shè)計等。 其次，研究了數(shù)據(jù)采集技術(shù)，并設(shè)計了數(shù)據(jù)采集系統(tǒng)，包括硬件系統(tǒng)設(shè)計和軟件系統(tǒng)設(shè)計兩大部分。硬件設(shè)計是在NI-PXI的基礎(chǔ)上選擇了數(shù)據(jù)采集平臺和數(shù)據(jù)采集卡以及相應(yīng)的線纜和調(diào)理設(shè)備并對其參數(shù)進(jìn)行了設(shè)定；軟件系統(tǒng)設(shè)計主要基于LabVIEW和MATLAB平臺設(shè)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(shù)，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(jìn)行信號預(yù)處理，包括去除奇異點(diǎn)處理和信號零均值處理；第二步，對經(jīng)過預(yù)處理的信號分別進(jìn)行時域分析、頻域分析和小波分析，并提取相應(yīng)的時頻特征值；第三步，對提取的時頻特征值進(jìn)行進(jìn)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(luò)的數(shù)控機(jī)床工作臺進(jìn)給系統(tǒng)的兩級故障診斷模型。第一級為總網(wǎng)絡(luò)，用來診斷不同部件的故障；第二級為各個子網(wǎng)絡(luò)，用來診斷同一部件的不同故障，分為滾動軸承網(wǎng)絡(luò)和滾珠絲杠網(wǎng)絡(luò)兩個子網(wǎng)絡(luò)。兩級故障診斷模型實現(xiàn)了故障的初步判別和故障的細(xì)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(xué)位授予單位】：青島理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)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(xué)位授予單位】：青島理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TG659;TH165.3
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