【摘要】：齒輪是機(jī)械設(shè)備中的一種通用傳動(dòng)零部件,廣泛應(yīng)用于現(xiàn)代機(jī)械中,但由于本身結(jié)構(gòu)復(fù)雜,工作環(huán)境惡劣等原因,齒輪極易出現(xiàn)故障,是一種易損件。在有齒輪裝置的傳動(dòng)機(jī)械中80%的故障與齒輪的故障有關(guān),在齒輪箱的各零件中,齒輪本身的故障比例最大,其故障率達(dá)60%以上。一旦機(jī)械設(shè)備出現(xiàn)齒輪故障,就可能中斷生產(chǎn),給企業(yè)造成巨大的經(jīng)濟(jì)損失,甚至帶來(lái)危害生命等災(zāi)難性后果;所以針對(duì)齒輪的機(jī)械故障診斷方法進(jìn)一步深入的研究具有重要的實(shí)際意義。本文為了降低噪聲對(duì)齒輪故障診斷的干擾影響,采用SVD降噪方法來(lái)提高故障診斷的準(zhǔn)確性。首先通過(guò)闡述基于Hankel矩陣SVD降噪方法的理論基礎(chǔ),采用常用的三種奇異值閾值處理方法,在旋轉(zhuǎn)機(jī)械振動(dòng)故障試驗(yàn)平臺(tái)上進(jìn)行實(shí)驗(yàn)測(cè)試,獲取齒輪故障振動(dòng)信號(hào),并通過(guò)三種方法信噪比與均方根誤差以及時(shí)頻域分析結(jié)果對(duì)比,從中發(fā)現(xiàn)奇異值中值方法降噪的效果比其它兩種方法更顯著,驗(yàn)證了SVD降噪方法在齒輪故障診斷中的可行性及有效性。其次,采用計(jì)算簡(jiǎn)捷、快速的排列熵方法對(duì)SVD降噪后齒輪故障的特征信息進(jìn)行提取,即將排列熵方法引入齒輪的故障診斷中,詳細(xì)闡述了排列熵算法及過(guò)程,分析了排列熵算法的特性,應(yīng)用MATLAB軟件對(duì)實(shí)驗(yàn)測(cè)試信號(hào)進(jìn)行相空間重構(gòu),并將計(jì)算獲得的排列熵值作為齒輪故障的特征向量,具有較好的抗噪性和突變檢測(cè)效果,從而驗(yàn)證了排列熵方法可作為齒輪故障狀態(tài)變化的定量依據(jù)。最后,采用支持向量機(jī)理論(SVM)作為智能診斷方法,研究了多類(lèi)SVM分類(lèi)器,利用常用的幾種核函數(shù)分別構(gòu)建了多類(lèi)SVM分類(lèi)器,使用齒輪不同狀態(tài)的排列熵特征向量,分別與這些多類(lèi)SVM分類(lèi)器相結(jié)合進(jìn)行訓(xùn)練,獲得齒輪典型的故障模式,從而可實(shí)現(xiàn)對(duì)齒輪故障進(jìn)行診斷及分類(lèi)。通過(guò)上述對(duì)比分析,可以發(fā)現(xiàn)采用徑向基核函數(shù)構(gòu)建的多類(lèi)SVM分類(lèi)器的分類(lèi)效果優(yōu)于其它兩種核函數(shù);同時(shí)將排列熵與神經(jīng)網(wǎng)絡(luò)相結(jié)合以及排列熵與支持向量機(jī)(SVM)相結(jié)合分別對(duì)齒輪故障進(jìn)行診斷,從對(duì)比分析結(jié)果,可以證明排列熵與支持向量機(jī)(SVM)相結(jié)合的方法對(duì)齒輪故障診斷的準(zhǔn)確度更高。
[Abstract]:Gear is a kind of universal transmission parts in mechanical equipment, which is widely used in modern machinery. However, because of its complicated structure and bad working environment, gears are prone to malfunction and are easily damaged. In the transmission machinery with gear device, 80% of the faults are related to the fault of the gear. Among the parts of the gear box, the proportion of the faults of the gear itself is the largest, and the failure rate of the gear itself is more than 60%. Once there is gear failure in mechanical equipment, the production may be interrupted, which will cause huge economic losses to enterprises, and even bring disastrous consequences such as endangering life and so on. Therefore, it is of great practical significance to further study the mechanical fault diagnosis method of gear. In order to reduce the influence of noise on gear fault diagnosis, SVD noise reduction method is used to improve the accuracy of fault diagnosis. In this paper, the theoretical foundation of SVD denoising method based on Hankel matrix is introduced, and three kinds of singular value threshold processing methods are used to test the vibration fault of rotating machinery on the platform of vibration test, and obtain the vibration signal of gear fault. By comparing the SNR of three methods with the root mean square error in time and frequency domain, it is found that the noise reduction effect of the median singular value method is more significant than that of the other two methods. The feasibility and effectiveness of SVD noise reduction method in gear fault diagnosis are verified. Secondly, a simple and fast permutation entropy method is used to extract the characteristic information of gear fault after SVD noise reduction. The permutation entropy method is introduced into gear fault diagnosis, and the algorithm and process of permutation entropy are described in detail. The characteristic of permutation entropy algorithm is analyzed, and the phase space reconstruction of experimental test signal is carried out by using MATLAB software, and the calculated permutation entropy value is taken as the characteristic vector of gear fault, which has good anti-noise and abrupt detection effect. It is verified that the permutation entropy method can be used as the quantitative basis of gear fault state change. Finally, the support vector machine (SVM) theory is used as the intelligent diagnosis method, and the multi-class SVM classifier is studied. The multi-class SVM classifier is constructed by using several common kernel functions, and the permutation entropy eigenvector of different gear states is used. Combined with these kinds of SVM classifiers, the typical fault modes of gears can be obtained, and the fault diagnosis and classification of gears can be realized. Through the above comparative analysis, it can be found that the classification effect of the multi-class SVM classifier constructed by radial basis function kernel function is better than that of the other two kernel functions. At the same time, combining permutation entropy with neural network and permutation entropy with support vector machine (SVM), the gear fault diagnosis is carried out, and the results are compared and analyzed. It can be proved that the combination of permutation entropy and support vector machine (SVM) is more accurate in gear fault diagnosis.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TH132.41

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