本文選題：旋轉(zhuǎn)機(jī)械 + 特征提取��； 參考：《哈爾濱工業(yè)大學(xué)》2012年碩士論文

【摘要】：隨著現(xiàn)代技術(shù)的發(fā)展，旋轉(zhuǎn)機(jī)械正在向著高速化、自動化方向發(fā)展，對機(jī)械關(guān)鍵部件的運(yùn)行可靠性提出了更高的要求，因而提高故障診斷的準(zhǔn)確性顯得愈發(fā)重要。故障特征提取是實(shí)現(xiàn)故障診斷的重要環(huán)節(jié)，能否有效提取故障信號特征直接影響故障診斷結(jié)果的正確與否，因此對旋轉(zhuǎn)機(jī)械信號特征提取的研究顯得十分必要。 本文首先介紹了國內(nèi)外對旋轉(zhuǎn)機(jī)械故障信號特征提取方法研究的現(xiàn)狀，引入相空間重構(gòu)理論，，對相空間重構(gòu)中兩個(gè)重要參數(shù)時(shí)間延遲和最小嵌入維數(shù)的選取進(jìn)行了研究，使用互信息法求時(shí)間延遲和虛假最鄰近點(diǎn)法求嵌入維數(shù)，同時(shí)介紹了遞歸圖和遞歸定量分析方法。 本文以旋轉(zhuǎn)機(jī)械軸承信號為研究對象，實(shí)現(xiàn)了基于小波變換的特征提取，基于遞歸定量分析的特征提取，以及基于小波變換與遞歸定量分析的特征提取。將提取的特征量作為輸入向量，利用概率神經(jīng)網(wǎng)絡(luò)對信號故障類型進(jìn)行了分類。 通過旋轉(zhuǎn)機(jī)械軸承故障實(shí)驗(yàn)，驗(yàn)證了遞歸定量分析對提取實(shí)際旋轉(zhuǎn)機(jī)械振動信號故障特征的有效性。故障的分類和評估實(shí)驗(yàn)結(jié)果表明，小波變換與遞歸定量結(jié)合的方法能更有效地提取信號的故障特征。 遞歸定量分析在振動信號故障特征提取中的有效性，為機(jī)械系統(tǒng)故障診斷提供了一種新的研究方式。遞歸定量分析過程中將故障信號的信息以簡單圖形的方式呈現(xiàn)使得故障信號直觀化，在理論和實(shí)際應(yīng)用上都有著重要的意義。
[Abstract]:With the development of modern technology, rotating machinery is developing towards the direction of high speed and automation, which puts forward higher requirements for the operation reliability of the key components of machinery, so it is more and more important to improve the accuracy of fault diagnosis. Fault feature extraction is an important part of fault diagnosis. Whether the fault signal features can be extracted effectively directly affects the correctness of fault diagnosis results. Therefore, it is very necessary to study the signal feature extraction of rotating machinery. This paper first introduces the present situation of research on fault signal feature extraction of rotating machinery at home and abroad, introduces the theory of phase space reconstruction, and studies the selection of two important parameters, time delay and minimum embedding dimension, in phase space reconstruction. Using mutual information method to find time delay and false nearest point method, the embedding dimension is obtained. The recursive graph and recursive quantitative analysis method are introduced at the same time. In this paper, the feature extraction based on wavelet transform, recursive quantitative analysis and wavelet transform and recursive quantitative analysis are realized. Using the extracted feature as input vector, the fault types of signals are classified using probabilistic neural networks. Through the fault test of rotating machinery bearing, the validity of recursive quantitative analysis to extract the fault characteristics of vibration signals of actual rotating machinery is verified. The experimental results of fault classification and evaluation show that the combination of wavelet transform and recursive quantification can extract the fault features more effectively. The effectiveness of recursive quantitative analysis in fault feature extraction of vibration signals provides a new research method for fault diagnosis of mechanical systems. In the process of recursive quantitative analysis, the information of fault signal is presented in a simple graphic way, which makes the fault signal intuitionistic, which is of great significance both in theory and in practice.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH133.3;TH165.3

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