本文關(guān)鍵詞：基于支持向量機的滾動軸承故障診斷方法研究　出處：《江西理工大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 滾動軸承 故障診斷 小波分析 支持向量機

【摘要】：軸承是旋轉(zhuǎn)機械的核心部件與易損件。一般工業(yè)用途的旋轉(zhuǎn)機械大多使用滾動軸承,在旋轉(zhuǎn)機械中起著非常重要的作用,對其進行監(jiān)測和故障診斷是學(xué)術(shù)界和產(chǎn)業(yè)界一直非常關(guān)注的課題。滾動軸承出現(xiàn)故障將可能導(dǎo)致設(shè)備的噪音和非正常振動,嚴重的將可能導(dǎo)致整臺設(shè)備乃至整條生產(chǎn)線都不能正常運行。因此,對滾動軸承的故障診斷進行研究具有重要的理論意義和應(yīng)用價值。 本文結(jié)合江西省自然科學(xué)基金項目的研究內(nèi)容,重點研究了基于小波包分析和支持向量機的滾動軸承故障診斷方法。借助設(shè)計的滾動軸承實驗臺以及數(shù)據(jù)采集系統(tǒng),進行了滾動軸承模擬故障試驗。在此基礎(chǔ)上研究了小波分析理論,利用小波包分析完成了對數(shù)據(jù)采集系統(tǒng)采集的原始振動信號數(shù)據(jù)的降噪和故障特征提取,最后結(jié)合在少樣本分類中具有明顯優(yōu)勢的支持向量機(Support Vector Machine,簡稱SVM)理論,提出基于小波包分析和支持向量機相結(jié)合的故障診斷方法。本文的主要工作： 1、首先研究了滾動軸承的振動機理、失效形式和檢測技術(shù),分析了常見檢測方法優(yōu)點和不足基礎(chǔ),采用測量滾動軸承的勺振動信號數(shù)據(jù)進行軸承的故障診斷,進而研究了滾動軸承的振動機理和特征頻率 2、分析了統(tǒng)計學(xué)習理論和支持向量機的基本思想,并將支持向量機引入到滾動軸承的故障診斷中,給出了支持現(xiàn)量機應(yīng)用于滾動軸承故障診斷的基本步驟和方法。 3、以QPZ-I故障模擬試驗平臺為對象,設(shè)計了試驗臺振動數(shù)據(jù)采集系統(tǒng),進行了滾動軸承內(nèi)圈、外圈和滾動體的故障模擬,為滾動軸承的故障特征提取提供診斷數(shù)據(jù)。 4、以模擬的滾動軸承故障數(shù)據(jù)為診斷對象,研究了基于小波分析的信號降噪和運用小波包分解提取故障特征的方法,結(jié)合Matlab平臺的優(yōu)勢,研究了基于小波包分解的滾動軸承信號特征提取方法,并進行實例進行了分析。 5、針對標準支持向量機不能直接用于解決故障診斷這種典型多值分類問題的不足,分析了支持向量機多值分類方法,采用二叉樹多值分類算法構(gòu)建分類器模型,結(jié)合小波包分解提取的振動故障特征向量,進行了滾動軸承的故障診斷,并探索了影響SVM分類精度的核參數(shù)的參數(shù)優(yōu)化。仿真結(jié)果驗證了支持向量用于滾動軸承故障診斷的正確性和有效性。 綜上所述,本文所研究的基于支持向量機的滾動軸承故障診斷方法是可行的,分析和診斷結(jié)果與實際吻合,能夠滿足滾動軸承故障診斷的要求,對滾動軸承的故障診斷具有一定的指導(dǎo)作用。
[Abstract]:The bearing is the key part of the rotating machinery and spare parts. General rotating machinery industrial uses most of the use of rolling bearings, plays a very important role in rotating machinery, the monitoring and fault diagnosis of the academia and industry have been very concerned about the issue. Rolling bearing failure may lead to equipment noise and non normal vibration, serious may cause the whole equipment and the whole production line can not run properly. Therefore, the research has important theoretical significance and application value for the fault diagnosis of rolling bearing.
This paper studies the content with the natural science foundation of Jiangxi Province, focuses on the research of wavelet packet analysis and support vector machine for rolling bearing fault diagnosis method based on the rolling bearing experimental design and data acquisition system for rolling bearing fault simulation test. Based on the theory of wavelet analysis, wavelet packet analysis the extraction of noise and fault characteristics of original vibration signal data acquisition system for data acquisition, finally combined with support vector machine has obvious advantages in small sample classification (the Support Vector Machine, referred to as SVM) theory, the fault diagnosis method of wavelet packet analysis and support vector machine based on the combination of the main work of this paper:
1, first study the vibration mechanism of rolling bearing, failure modes and detection technology, analyzes the advantages and disadvantages of common detection methods, the data measured by vibration signal of rolling bearing fault diagnosis of bearing, and then studied the vibration mechanism and characteristic frequency of rolling bearing
2, analysis of the basic theory of statistical learning theory and support vector machine, and the support is introduced into the fault diagnosis of rolling bearing in support vector machine, given the amount of machine used in rolling bearing fault diagnosis method and basic steps.
3, taking the QPZ-I fault simulation test platform as the object, we designed the test platform vibration data acquisition system, carried out the fault simulation of rolling bearing inner ring, outer ring and rolling element, and provided diagnostic data for rolling bearing's fault feature extraction.
4, the data simulation for rolling bearing fault diagnosis object, studies the signal denoising of wavelet analysis and wavelet packet decomposition method to extract fault features based on the combination of the advantages of Matlab platform, the wavelet packet method to extract signal of rolling bearing based on eigen decomposition, and the example is analyzed.
5, according to the standard support vector machine can not be directly used to solve the fault diagnosis of the typical multi valued classification problems, analyzes the classification method of multi valued support vector machine, using two binary tree multi value classification model classification algorithm based on wavelet packet decomposition to extract the fault feature of vibration vector, the fault diagnosis of rolling bearing, and to explore the effect of parameter optimization of kernel parameters for SVM classification accuracy. The simulation results verify the support vector for rolling bearing fault diagnosis is correct and effective.
To sum up, the support vector machine based rolling bearing fault diagnosis method is feasible, and the analysis and diagnosis results coincide with the reality, which can meet the requirements of rolling bearing fault diagnosis, and has a guiding role in the rolling bearing fault diagnosis.

【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3;TH133.33

【引證文獻】

相關(guān)期刊論文 前1條

1 張寶俊;裴小龍;;芻議煉化企業(yè)電動機滾動軸承故障及保養(yǎng)方法[J];電子制作;2013年13期

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本文編號：1400630