本文選題：電主軸 + 軸承　； 參考：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：電主軸是數(shù)控機(jī)床核心的部件之一,其性能的優(yōu)劣直接影響到機(jī)床的加工精度。對電主軸進(jìn)行故障診斷能提高數(shù)控機(jī)床加工的可靠性,具有重大的意義。本文以數(shù)控機(jī)床電主軸為研究對象,通過經(jīng)驗?zāi)B(tài)分析和支持向量機(jī)等方法,開展其故障診斷方法的研究,并搭建故障診斷平臺。主要工作如下:(1)對電主軸的結(jié)構(gòu)進(jìn)行了分析,并結(jié)合企業(yè)提供的維修歷史數(shù)據(jù),對電主軸的故障進(jìn)行統(tǒng)計分析,將電主軸中故障頻發(fā)、危害性較大的軸承故障作為本次研究的對象。(2)對軸承的故障機(jī)理,包括振動機(jī)理、失效模式、特征頻率等進(jìn)行了分析研究,并總結(jié)了各類典型故障的振動特性;將小波降噪、經(jīng)驗?zāi)B(tài)分析、峭度和相關(guān)系數(shù)判別方法結(jié)合,進(jìn)行故障信號的特征頻率提取,并通過在實驗數(shù)據(jù)中的成功應(yīng)用驗證了該信號處理方法的有效性。(3)結(jié)合經(jīng)驗?zāi)B(tài)分析和自回歸模型,優(yōu)化了支持向量機(jī)特征向量的構(gòu)建方法;通過數(shù)據(jù)預(yù)處理方法(歸一化和降維)和參數(shù)尋優(yōu)方法(窮舉法、遺傳算法和粒子群算法)的分析研究,將實驗數(shù)據(jù)的診斷模型的測試準(zhǔn)確率從87.8%提升至95.6%,驗證了數(shù)據(jù)預(yù)處理和參數(shù)尋優(yōu)能夠有效提升診斷模型的性能。(4)最后,針對企業(yè)的一臺龍門銑床的電主軸,搭建了故障診斷平臺,驗證了診斷方案的有效性。綜上所述,本文提出的基于支持向量機(jī)的電主軸故障診斷方法,具有較好的實用性。
[Abstract]:Motorized spindle is one of the core components of CNC machine tools, and its performance directly affects the machining accuracy of machine tools. The fault diagnosis of motorized spindle can improve the reliability of NC machine tool, and it is of great significance. This paper takes the motorized spindle of NC machine tool as the research object, through empirical modal analysis and support vector machine and so on, carries out the research of its fault diagnosis method, and builds the fault diagnosis platform. The main work is as follows: (1) the structure of the motorized spindle is analyzed, and combined with the maintenance history data provided by the enterprise, the failure of the motorized spindle is statistically analyzed, and the faults in the motorized spindle are frequently occurred. As the object of this study, bearing failure mechanism, including vibration mechanism, failure mode, characteristic frequency and so on, is analyzed and studied, and the vibration characteristics of various typical faults are summarized. Empirical modal analysis, kurtosis and correlation coefficient discrimination are combined to extract the characteristic frequency of fault signal. Through the successful application in the experimental data, the validity of the signal processing method is verified. 3) combining with the empirical modal analysis and the autoregressive model, the feature vector construction method of the support vector machine is optimized. Through the analysis of data preprocessing methods (normalization and dimension reduction) and parameter optimization methods (exhaustive method, genetic algorithm and particle swarm optimization), The test accuracy of the diagnostic model of experimental data was raised from 87.8% to 95.6B, which proved that data preprocessing and parameter optimization could effectively improve the performance of the diagnostic model. Finally, for the motorized spindle of a gantry milling machine in the enterprise, the test accuracy of the diagnostic model was improved from 87.8% to 95.6%. A fault diagnosis platform is built to verify the effectiveness of the diagnosis scheme. To sum up, the method of motor spindle fault diagnosis based on support vector machine proposed in this paper has good practicability.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG659

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