本文選題：滾動軸承 + 故障診斷��； 參考：《大連理工大學》2013年博士論文

【摘要】：滾動軸承是機械設(shè)備中使用量最多的關(guān)鍵零部件,保證滾動軸承正常運行是設(shè)備維護工作的重要內(nèi)容。但滾動軸承工作狀態(tài)復雜,轉(zhuǎn)速變化大,承載方式多樣,運動形式多變,這些都會對滾動軸承故障診斷產(chǎn)生不利的影響,從而降低各種傳統(tǒng)診斷方法的效能。為此,本文以滾動軸承故障振動信號為研究對象,將流形學習方法與現(xiàn)代信號處理理論結(jié)合,對滾動軸承故障診斷過程中遇到的降噪、特征提取、故障源分離、性能監(jiān)測問題進行研究。論文的主要研究內(nèi)容及結(jié)論如下： 1.論述了開展?jié)L動軸承故障診斷的意義,分析了滾動軸承故障診斷技術(shù)不同發(fā)展階段的特點。對滾動軸承故障診斷中遇到的降噪、特征提取、故障源分離、性能監(jiān)測問題的研究現(xiàn)狀進行了綜述。在總結(jié)滾動軸承故障診斷技術(shù)發(fā)展趨勢的基礎(chǔ)上,介紹了非線性流形學習理論及其在故障診斷中的應用。 2.針對滾動軸承故障信號降噪問題,提出了一種基于最大方差展開(MVU)算法的對偶樹復小波(DTCWT)降噪方法。利用MVU提取DTCWT細節(jié)信號空間的信號子空間,去除噪聲子空間實現(xiàn)降噪。DTCWT具備平移不變性和完全重構(gòu)性,能克服常規(guī)小波變換平移敏感性和非完全重構(gòu)的缺陷。MVU流形算法能有效提取高維數(shù)據(jù)空間的非線性結(jié)構(gòu),克服線性結(jié)構(gòu)的不足。結(jié)合二者優(yōu)勢的DTCWT_MVU降噪方法,比傳統(tǒng)降噪方法具有更高的信噪比,能更好地提取故障信號的非線性沖擊成分,減少降噪后信號波形的失真。仿真和工程實際信號驗證了該方法的有效性。 3.對于滾動軸承故障特征提取問題,提出了一種基于張量流形學習的時頻故障特征提取方法。在HHT時頻特征的基礎(chǔ)上,利用張量流形學習方法提取信號的非線性張量流形時頻特征,定義了時頻特征參數(shù),將張量流形時頻特征參數(shù)與概率神經(jīng)網(wǎng)絡(luò)相結(jié)合,準確實現(xiàn)了軸承故障樣本分類。張量流形學習能有效提取高維時頻特征組合的內(nèi)蘊非線性特征,與HHT時頻特征參數(shù)相比,張量流形時頻特征參數(shù)能減少特征信息的冗余,更有效地區(qū)分不同類型故障樣本,降低神經(jīng)網(wǎng)絡(luò)的迭代次數(shù),提高故障分類的準確性。 4.對于滾動軸承故障源分離問題,提出了一種基于流形學習的滾動軸承故障源盲分離方法。利用EMD分解構(gòu)造了多通道測試信號,估計測試信號的信源數(shù),建立最優(yōu)測試信號的選擇標準,綜合利用峭度、稀疏度、互信息標準選擇最優(yōu)測試信號,通過提取最優(yōu)測試信號的KPCA流形成分作為ICA算法的輸入,有效分離出故障源。該方法解決了欠定盲分離過程中最優(yōu)測試信號的選取問題,利用流形學習增強了ICA的分離能力,使其能從故障信息微弱的單通道信號中分離出沖擊特征明顯的故障源。 5.針對滾動軸承性能退化監(jiān)測問題,提出了一種基于流形學習和模糊聚類的性能監(jiān)測方法。利用小波包分解確定監(jiān)測信號的敏感頻帶,在此基礎(chǔ)上提取信號的低維流形特征作為模糊聚類的數(shù)據(jù)樣本,以樣本的隸屬度值作為性能指標,監(jiān)測軸承性能退化規(guī)律。與基于單特征及線形多特征的監(jiān)測方法相比,該方法能有效體現(xiàn)滾動軸承全壽命性能退化周期的四個階段,反映滾動軸承各部件性能退化的統(tǒng)一規(guī)律,提前預知軸承早期故障。 6.使用LabVIEW和MATLAB混合編程的方式開發(fā)了基于流形學習的滾動軸承故障分析診斷系統(tǒng)。介紹了系統(tǒng)開發(fā)的軟硬件環(huán)境和結(jié)構(gòu)方案,通過實例演示了系統(tǒng)的基本功能,驗證了系統(tǒng)的有效性。
[Abstract]:Rolling bearing is the most important component in mechanical equipment. It is an important part of the maintenance work to ensure the normal running of the rolling bearing. But the working state of the rolling bearing is complex, the rotational speed is varied, the bearing mode is varied, and the form of movement is changeable. All these will have an adverse effect on the rolling bearing fault diagnosis, thus reducing a variety of factors. In this paper, this paper takes the rolling bearing fault vibration signal as the research object, combines the manifold learning method with the modern signal processing theory, and studies the noise reduction, feature extraction, fault source separation and performance monitoring problems encountered in the fault diagnosis of rolling bearings. The main contents and conclusions of this paper are as follows :
1. the significance of rolling bearing fault diagnosis is discussed, and the characteristics of different development stages of rolling bearing fault diagnosis technology are analyzed. The research status of noise reduction, feature extraction, fault source separation and performance monitoring problems encountered in rolling bearing fault diagnosis are summarized. The basis for summarizing the development trend of rolling bearing fault diagnosis technology is summarized. The nonlinear manifold learning theory and its application in fault diagnosis are introduced.
2. a dual tree complex wavelet (DTCWT) denoising method based on the maximum variance expansion (MVU) algorithm is proposed to reduce the noise of rolling bearings fault signal. Using MVU to extract the signal subspace of the DTCWT detail signal space, the noise subspace is removed to realize the flat shift invariance and complete reconstruction of the noise reduction.DTCWT, and the conventional wavelet transform can be overcome. The defect.MVU manifold algorithm can effectively extract the nonlinear structure of the high dimensional data space and overcome the shortage of linear structure. The DTCWT_MVU denoising method combining the two advantages has a higher signal to noise ratio than the traditional noise reduction method, and can better extract the nonlinear impact component of the obstacle signal and reduce the noise reduction after reducing the noise. The distortion of signal waveform is verified by simulation and engineering practical signals.
3. for the problem of rolling bearing fault feature extraction, a time frequency fault feature extraction method based on tensor manifold learning is proposed. Based on the time-frequency characteristics of HHT, the tensor manifold learning method is used to extract the time-frequency characteristics of the nonlinear tensor flow manifolds of the signal, and the time frequency characteristic parameters are defined, and the time frequency characteristic parameters and the probability of the tensor manifolds are given. With the combination of neural network, the classification of bearing fault samples is accurately realized. The tensor manifold learning can effectively extract the intrinsic nonlinear characteristics of the combination of high dimensional frequency characteristics. Compared with the HHT time-frequency characteristic parameters, the tensor manifold time frequency characteristic parameters can reduce the redundancy of the feature information, divide the different types of fault samples more effectively, and reduce the neural network. The number of iterations can improve the accuracy of the fault classification.
4. for the problem of fault source separation of rolling bearings, a blind separation method of rolling bearing fault source based on manifold learning is proposed. The multi channel test signal is constructed by EMD decomposition, the number of source of the test signal is estimated, the selection standard of the optimal test signal is established, and the optimal test letter is selected by comprehensive use of kurtosis, sparsity and mutual information standard. By extracting the KPCA manifold component of the optimal test signal as the input of the ICA algorithm, the fault source is effectively separated. The method solves the problem of selecting the optimal test signal in the underdetermined blind separation process. The separation ability of the ICA is enhanced by manifold learning, and the impact feature can be separated from the weak single channel signal with the weak fault signal. An obvious source of failure.
5. in view of the performance degradation monitoring of rolling bearings, a performance monitoring method based on manifold learning and fuzzy clustering is proposed. The wavelet packet decomposition is used to determine the sensitive frequency band of the monitoring signal. On this basis, the feature of the low dimensional manifold of the signal is extracted as the data sample of the fuzzy clustering, and the membership degree value of the sample is used as the performance index. Compared with the single feature and linear multi feature monitoring method, the method can effectively reflect the four stages of the life performance degradation period of the rolling bearing, reflect the unified law of the performance degradation of the rolling bearing components, and advance the early prediction of the bearing barrier.
6. the fault analysis and diagnosis system of rolling bearing based on manifold learning is developed using the hybrid programming of LabVIEW and MATLAB. The software and hardware environment and the structure scheme of the system development are introduced. The basic functions of the system are demonstrated by an example, and the effectiveness of the system is verified.
【學位授予單位】：大連理工大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：TH165.3;TH133.33

【引證文獻】

相關(guān)博士學位論文 前1條

1 張紹輝;基于流形學習的機械狀態(tài)識別方法研究[D];華南理工大學;2014年

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本文編號：1930620