本文選題：小波分析 + Mallat算法　； 參考：《大連交通大學(xué)》2015年碩士論文

【摘要】：隨著現(xiàn)代社會的發(fā)展與進(jìn)步,人們越來越多地認(rèn)識到機(jī)械的安全運(yùn)行對于工業(yè)生產(chǎn)的重要性。正因如此,故障診斷方面的技術(shù)應(yīng)用得到了大力推廣,而傳統(tǒng)的診斷技術(shù)則越來越難滿足人們對于機(jī)械不斷發(fā)展的安全標(biāo)準(zhǔn)。在過去的診斷方法中,人們最常使用快速傅里葉變換技術(shù)來對故障信號進(jìn)行提取。這種方法為診斷行為提供了極大的便利,如果信號的特性是平穩(wěn)的,則用該技術(shù)進(jìn)行診斷時(shí)并不會發(fā)生異常。一旦信號具有時(shí)變特性,或是原信號夾雜著瞬時(shí)變化,那么這一類基于FFT技術(shù)的方法就會顯現(xiàn)出弊端。在多分辨分析領(lǐng)域,小波變換可謂是一種新興技術(shù),它的核心是振蕩的、衰減的基函數(shù),其可以分析任意小的頻率特征,因而被人們比作“數(shù)學(xué)顯微鏡”。本文首先從課題的背景出發(fā),論述了故障診斷技術(shù)在國內(nèi)外發(fā)展的現(xiàn)狀。在對比經(jīng)典的故障診斷方法和小波變化方法優(yōu)劣之前,本文先對旋轉(zhuǎn)機(jī)械常見的故障及其機(jī)理做了比較全面的總結(jié),從而建立了機(jī)械故障與信號特征提取之間的聯(lián)系。最后探討了在診斷旋轉(zhuǎn)機(jī)械的過程中利用小波分解技術(shù)的Mallat算法的問題,并在此基礎(chǔ)上進(jìn)行FFT變換,從而提高頻譜特征的提取能力。仿真實(shí)驗(yàn)的結(jié)果說明,這種方法確實(shí)改善了故障診斷的精確性,得到了我們所期待的效果。
[Abstract]:With the development and progress of modern society, more and more people realize the importance of safe operation of machinery to industrial production. Because of this, the application of fault diagnosis technology has been popularized greatly, but the traditional diagnosis technology is more and more difficult to meet the safety standards of the continuous development of machinery. In the past, fast Fourier transform (FFT) is used to extract fault signals. This method provides great convenience for diagnosis behavior. If the signal characteristics are stable, there will be no anomalies when using this technique. Once the signal has the characteristic of time-varying or the original signal is mixed with the instantaneous variation, then this kind of method based on FFT technology will show some disadvantages. In the field of Multiresolution analysis, wavelet transform is a new technology. Its core is oscillating and decaying basis function, which can analyze any small frequency characteristic, so it is compared to "mathematical microscope". At first, this paper discusses the development of fault diagnosis technology at home and abroad from the background of the subject. Before comparing the advantages and disadvantages of the classical fault diagnosis method and the wavelet change method, the common faults and their mechanism of rotating machinery are summarized in this paper, and the relationship between the fault of machinery and the feature extraction of signal is established. Finally, the problem of Mallat algorithm based on wavelet decomposition in the diagnosis of rotating machinery is discussed, and on the basis of this, FFT transform is carried out to improve the ability of spectrum feature extraction. The simulation results show that this method can improve the accuracy of fault diagnosis and get the desired results.
【學(xué)位授予單位】：大連交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TE65;TQ050.7

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