發(fā)布時(shí)間：2018-02-03 10:13

  本文關(guān)鍵詞： 滾動(dòng)軸承 排列熵 極限學(xué)習(xí)機(jī) 最大相關(guān)最小冗余 故障診斷　出處：《昆明理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來(lái),我國(guó)科技事業(yè)得到了進(jìn)一步提升,信息技術(shù)的發(fā)展帶動(dòng)了工業(yè)化的進(jìn)步,使得機(jī)械化生產(chǎn)日益普及。在機(jī)械的生產(chǎn)過(guò)程與應(yīng)用中,機(jī)械系統(tǒng)的非計(jì)劃停機(jī)與故障停機(jī)會(huì)給企業(yè)的生產(chǎn)發(fā)展及經(jīng)濟(jì)效益帶來(lái)嚴(yán)重的損害,甚至存有人身安全隱患,造成安全事故。在旋轉(zhuǎn)機(jī)械設(shè)備的眾多零部件中,地位最高但又是最容易損壞的部件就是軸承。軸承是否能正常運(yùn)行直接影響整個(gè)機(jī)械系統(tǒng)的性能與壽命,所以對(duì)滾動(dòng)軸承故障診斷的方法進(jìn)行研究具有重大意義。滾動(dòng)軸承振動(dòng)信號(hào)多表現(xiàn)為非平穩(wěn)性與非線性,同時(shí),受周邊設(shè)備傳來(lái)的噪聲以及故障信息中所含有的短期沖擊成分的影響,使得故障特征提取有一定的難度。特征提取不全面、特征值不明顯,均會(huì)影響軸承的故障識(shí)別精度,帶來(lái)誤判甚至漏判現(xiàn)象。針對(duì)此問(wèn)題,本文提出了基于多特征量提取和極限學(xué)習(xí)機(jī)的軸承故障診斷方法研究。論文的主要研究工作如下:(1)研究了滾動(dòng)軸承故障的形成原因以及其振動(dòng)頻率,具體分析了軸承的結(jié)構(gòu)和動(dòng)力學(xué)特性,提出了幾種典型振動(dòng)的基本參數(shù)。對(duì)滾動(dòng)軸承故障數(shù)據(jù)采集系統(tǒng)進(jìn)行學(xué)習(xí)和研究,獲得不同類型下的軸承振動(dòng)數(shù)據(jù),最后,以Logistic典型非線性系統(tǒng)為驗(yàn)證對(duì)象,進(jìn)而驗(yàn)證排列熵用于檢測(cè)非線性系統(tǒng)的動(dòng)力學(xué)突變行為的可行性。(2)研究了基于排列熵和極限學(xué)習(xí)機(jī)的軸承故障類型診斷方法。通過(guò)使用多分辨奇異值分解對(duì)原始加速度振動(dòng)信號(hào)進(jìn)行分解,獲得三層不同細(xì)節(jié)分量D1～D3,進(jìn)一步結(jié)合排列熵在信息提取方面的優(yōu)勢(shì),構(gòu)造能表征原始加速度振動(dòng)信號(hào)故障特征的特征向量,最后采用極限學(xué)習(xí)機(jī)的方法進(jìn)行軸承故障類型識(shí)別,驗(yàn)證了該方法的可行性和有效性。(3)研究了基于優(yōu)化MRMR和極限學(xué)習(xí)機(jī)的軸承故障類型診斷方法。對(duì)經(jīng)過(guò)多分辨SVD降噪處理的原始加速度振動(dòng)信號(hào)分別求取時(shí)域、頻域和時(shí)-頻域特征量構(gòu)建混合域特征集,在特征選取上,采用加權(quán)MRMR的特征選取方式,以極限學(xué)習(xí)機(jī)分類正確率為依據(jù),從含有18個(gè)特征的混合域中最后選取出8個(gè)最優(yōu)特征向量。實(shí)驗(yàn)數(shù)據(jù)分析顯示,故障辨別精度可達(dá)到97.5%,證明該方法可以有效的實(shí)現(xiàn)軸承故障類型診斷。以實(shí)際滾動(dòng)軸承故障數(shù)據(jù)為例進(jìn)行對(duì)比分析,結(jié)果表明基于優(yōu)化MRMR和極限學(xué)習(xí)機(jī)的軸承故障智能診斷方法比基于排列熵和極限學(xué)習(xí)機(jī)的故障診斷方法的故障識(shí)別率高,這是由于相比于特征值單一且特征不明顯的排列熵,混合域特征集更能多方面表征振動(dòng)信號(hào)的內(nèi)在特征,且經(jīng)過(guò)優(yōu)化的MRMR算法準(zhǔn)則選出來(lái)的特征子集是最具代表性的。
[Abstract]:In recent years, China's science and technology has been further promoted, the development of information technology has led to the progress of industrialization, making mechanized production increasingly popular in the production process and application of machinery. The unplanned downtime and malfunction of the mechanical system will bring serious damage to the production development and economic benefit of the enterprise, even have the personal safety hidden trouble, cause the safety accident, in many parts and components of the rotating machinery and equipment. Bearing is the highest-ranking but most easily damaged component. Whether the bearing can run normally will directly affect the performance and life of the whole mechanical system. Therefore, it is of great significance to study the fault diagnosis method of rolling bearings. The vibration signals of rolling bearings are usually non-stationary and nonlinear, and at the same time. Affected by the noise from peripheral equipments and the short term impact components contained in the fault information, it is difficult to extract the fault features. The feature extraction is not comprehensive, and the feature value is not obvious. Will affect the bearing fault identification accuracy, leading to misjudgment or even miss the phenomenon, in view of this problem. In this paper, the method of bearing fault diagnosis based on multi-feature extraction and extreme learning machine is proposed. The main research work of this paper is as follows: 1) the cause of rolling bearing fault and its vibration frequency are studied. The structure and dynamic characteristics of the bearing are analyzed in detail, and the basic parameters of several typical vibration are put forward. The fault data acquisition system of rolling bearing is studied and studied, and the bearing vibration data of different types are obtained. Finally, the typical nonlinear Logistic system is used as the verification object. Furthermore, the feasibility of using permutation entropy to detect the dynamical catastrophe behavior of nonlinear systems is verified. The fault type diagnosis method of bearing based on permutation entropy and ultimate learning machine is studied. The original acceleration vibration signal is decomposed by using multi-resolution singular value decomposition. Three layers of different detail components D _ 1 and D _ 3 are obtained. Furthermore, combining the advantage of permutation entropy in information extraction, the eigenvector which can represent the fault characteristics of the original acceleration vibration signal is constructed. Finally, the bearing fault type is identified by the extreme learning machine. The feasibility and effectiveness of the method are verified. The fault type diagnosis method of bearing based on optimized MRMR and ultimate learning machine is studied, and the time domain of the original acceleration vibration signal after multi-resolution SVD de-noising is obtained respectively. In frequency domain and time-frequency domain, the feature sets in mixed domain are constructed. In feature selection, weighted MRMR is used to select features, and the classification accuracy of extreme learning machine is taken as the basis. Finally, 8 optimal feature vectors are selected from the mixed domain with 18 features. The experimental data show that the accuracy of fault identification can reach 97.5%. It is proved that this method can effectively realize the fault type diagnosis of bearing. Take the actual rolling bearing fault data as an example to carry on the contrast analysis. The results show that the intelligent fault diagnosis method based on optimized MRMR and LLM is higher than that based on permutation entropy and LLM. This is due to the fact that, compared with the permutation entropy with a single eigenvalue and no obvious feature, the feature set in the mixed domain can represent the intrinsic characteristics of the vibration signal in many ways. And the feature subset selected by the optimized MRMR algorithm criterion is the most representative.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH133.33;TP181

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