【摘要】：隨著傳感器技術(shù)、測試技術(shù)及信號分析的發(fā)展，故障診斷技術(shù)有了較大提高，其主要研究內(nèi)容包括四個方面：信號采集、信號特征提取、故障診斷、信息融合。其中，信號特征提取是指故障診斷過程中獲取與系統(tǒng)狀態(tài)相關(guān)性較大的敏感特征的特征因子提取技術(shù)。特征提取的正確與否將直接影響故障診斷結(jié)果的準(zhǔn)確性。針對傳統(tǒng)信號分析方法難以準(zhǔn)確描述轉(zhuǎn)子振動信號非平穩(wěn)特性以及信號特征難以定量評價(jià)的問題，本研究提出一種經(jīng)驗(yàn)?zāi)B(tài)分解和信息熵相結(jié)合的方法，對轉(zhuǎn)子故障振動信號特征提取和定量評價(jià)方法進(jìn)行研究。 針對轉(zhuǎn)子實(shí)驗(yàn)臺采集到的數(shù)據(jù)，開展的主要內(nèi)容及研究成果如下： 1)以軸承-轉(zhuǎn)子系統(tǒng)模型為基礎(chǔ)，研究旋轉(zhuǎn)機(jī)械常見的故障類型及機(jī)理。針對轉(zhuǎn)子故障信號的特點(diǎn)，，設(shè)計(jì)了中值濾波和小波消噪結(jié)合的故障信號預(yù)處理濾波器，為特征提取提供原始數(shù)據(jù)。重點(diǎn)研究了本文的信號處理方法—經(jīng)驗(yàn)?zāi)B(tài)分解法(EMD)，分析該方法的實(shí)質(zhì)及特點(diǎn)。針對EMD分解過程中存在的迭代次數(shù)難以確定及端點(diǎn)效應(yīng)問題，提出了“能量算法”和“相似信號平移算法”相結(jié)合的算法。通過對仿真實(shí)驗(yàn)信號的分析表明，該算法能夠?qū)σ陨蠁栴}準(zhǔn)確、有效地解決。 2)分析信息熵在故障診斷中的研究現(xiàn)狀，對時域奇異譜熵、頻域功率譜熵、時-頻域小波能譜熵和小波空間狀態(tài)譜熵進(jìn)行了比較系統(tǒng)的研究和分析，并基于LabVIEW編寫信息熵算法程序。 3)提出了一種基于經(jīng)驗(yàn)?zāi)B(tài)分解法的轉(zhuǎn)子故障信號熵特征提取方法。提出“能量法”與“相關(guān)系數(shù)法”相結(jié)合的算法選取出包含主要故障信息的分量。實(shí)驗(yàn)表明，該方法能準(zhǔn)確地提取出后續(xù)研究所需的特征數(shù)據(jù)。再計(jì)算出包含主要故障的分量的四種信息熵值，即時域奇異譜熵、頻域功率譜熵、時-頻域小波能譜熵和小波空間狀態(tài)譜熵。實(shí)驗(yàn)信號的分析結(jié)果表明，該方法能夠較好的實(shí)現(xiàn)對轉(zhuǎn)子系統(tǒng)故障信號的量化特征提取，所提取出的特征集合具有能夠使典型故障特征量之間存在顯著差異的性能。 4)基于信息融合的思想，計(jì)算EMD分解后所選取出主要故障分量的奇異譜熵、功率譜熵及小波能譜熵，提出建立故障信號信息熵狀態(tài)空間分布圖。實(shí)驗(yàn)結(jié)果表明，該狀態(tài)空間模型能夠直觀、準(zhǔn)確地實(shí)現(xiàn)轉(zhuǎn)子故障的模式識別。 5)以LabVIEW軟件為平臺，建立了典型轉(zhuǎn)子故障信號測試系統(tǒng)。該系統(tǒng)實(shí)現(xiàn)了對采集到的原始信號濾波處理及對原始信號和濾波后信號的頻譜分析和軸心軌跡的分析。 本文以轉(zhuǎn)子故障信號量化特征提取為目的，針對涉及到的數(shù)字信號處理、信息論以及智能診斷理論等內(nèi)容，本文的研究工作值得進(jìn)一步深入。
[Abstract]:With the development of sensor technology, test technology and signal analysis, fault diagnosis technology has been greatly improved. The main research contents include four aspects: signal acquisition, signal feature extraction, fault diagnosis, information fusion. In the process of fault diagnosis, signal feature extraction refers to the feature factor extraction technique, which can obtain sensitive features which are highly correlated with the system state. Whether the feature extraction is correct or not will directly affect the accuracy of fault diagnosis results. The traditional signal analysis method is difficult to accurately describe the non-stationary characteristics of rotor vibration signals and it is difficult to quantitatively evaluate the signal characteristics. In this paper, an empirical mode decomposition method combined with information entropy is proposed. The method of feature extraction and quantitative evaluation of rotor fault vibration signal is studied. The main contents and research results are as follows: 1) based on the bearing-rotor system model, the common fault types and mechanism of rotating machinery are studied. According to the characteristics of rotor fault signal, a fault signal preprocessing filter combining median filter and wavelet de-noising is designed, which provides the original data for feature extraction. In this paper, the essence and characteristics of the signal processing method-empirical mode decomposition method (EMD),) are studied. In view of the difficulty of determining the number of iterations and the endpoints effect in the process of EMD decomposition, a combination of "energy algorithm" and "similar signal translation algorithm" is proposed. The analysis of simulation signals shows that the algorithm can solve the above problems accurately and effectively. 2) the research status of information entropy in fault diagnosis is analyzed, and the singular spectrum entropy in time domain and power spectrum entropy in frequency domain are analyzed. The wavelet energy spectrum entropy in time-frequency domain and the state spectrum entropy in wavelet space are studied and analyzed systematically. An information entropy algorithm program based on LabVIEW is developed. 3) an information entropy feature extraction method based on empirical mode decomposition (EMD) is proposed. An algorithm combining "energy method" and "correlation coefficient method" is proposed to select the components containing the main fault information. The experimental results show that the method can extract the characteristic data exactly. Four kinds of information entropy values including the components of the main faults are calculated including the instantaneous singular spectral entropy the frequency-domain power spectral entropy the time-frequency-domain wavelet energy spectrum entropy and the wavelet spatial state spectral entropy. The experimental results show that the proposed method can extract the quantized feature of rotor system fault signal well. The extracted feature set has the capability of making significant difference between the typical fault feature quantities. 4) based on the idea of information fusion, the singular spectral entropy of the main fault components selected by EMD decomposition is calculated. Based on power spectrum entropy and wavelet energy spectrum entropy, a state space distribution map of information entropy of fault signal is proposed. The experimental results show that the state space model can realize the rotor fault pattern recognition intuitively and accurately. 5) based on LabVIEW software, a typical rotor fault signal testing system is established. The system realizes the filtering processing of the original signal, the spectrum analysis of the original signal and the filtered signal and the analysis of the axis locus. The aim of this paper is to extract the quantized feature of rotor fault signal. The research work in this paper is worthy of further research on digital signal processing, information theory and intelligent diagnosis theory.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 孫茂軍;汽車機(jī)械變速器動力性能試驗(yàn)臺的研究[D];武漢理工大學(xué);2013年

本文編號：2219237