本文選題：故障診斷 + 轉(zhuǎn)子系統(tǒng)��； 參考：《蘭州理工大學(xué)》2012年碩士論文

【摘要】：在故障診斷過程中，故障信號特征量提取和故障模式識別是整個過程的關(guān)鍵步驟。基于此，本文將一種新的時頻分析方法局部均值分解（Local MeanDecomposition，簡稱LMD）和另一種基于統(tǒng)計學(xué)的模式識別技術(shù)隱Markov模型（hidden Markov model，簡稱HMM）應(yīng)用于轉(zhuǎn)子系統(tǒng)的故障診斷中。 本文模擬了轉(zhuǎn)子試驗臺常見故障，為提高特征提取的信息量，采用基于LMD的方法對故障信號進(jìn)行特征提取，同時，使用了統(tǒng)計理論十分嚴(yán)謹(jǐn)?shù)腍MM方法進(jìn)行故障模式識別。圍繞試驗臺提取的多種故障信號數(shù)據(jù)，用以上理論以及算法，本文做出的主要工作和結(jié)論如下： （1）在轉(zhuǎn)子試驗臺上分別模擬出四種典型轉(zhuǎn)子故障振動信號，對故障信號的振動機(jī)理和過程進(jìn)行了深入研究，并且進(jìn)行了小波濾波消噪和頻譜分析。實驗證明，小波消噪后得到的信號比較平滑，頻譜圖的各頻率成分十分明顯，很適合轉(zhuǎn)子系統(tǒng)的數(shù)據(jù)處理，可以為特征提取提供原始數(shù)據(jù)。 （2）提出了一種基于LMD和近似熵的故障特征提取新方法，LMD的局域化特征方法可以自適應(yīng)分解信號為多個有用的乘積函數(shù)（Product function，簡稱PF）和一個余量之和，再結(jié)合改進(jìn)的快速近似熵計算方法計算出每個PF的近似熵作為故障特征值，綜合所有故障特征值組合為特征向量集。實驗結(jié)果表明，基于LMD和近似熵的故障特征向量集可以準(zhǔn)確反映故障信號特征，并且適于輸入至HMM分類器進(jìn)行故障分類。 （3）以構(gòu)造最優(yōu)分類器為目標(biāo)，針對HMM找到全局最優(yōu)點的概率相對較低的問題，提出了一種經(jīng)粒子群算法（Particle Swarm Optimization，簡稱PSO）優(yōu)化的HMM的模式分類器。該方法可以優(yōu)化HMM訓(xùn)練模型，使之準(zhǔn)確尋找全局最優(yōu)概率。將齒輪箱故障數(shù)據(jù)提取的幅值作為特征向量輸入到該分類器中進(jìn)行數(shù)據(jù)分類，實驗表明，收斂誤差較小，并可以成功識別齒輪箱故障。 （4）提出基于LMD近似熵和PSO優(yōu)化的HMM診斷方法，對轉(zhuǎn)子試驗臺上采集的故障信號進(jìn)行故障特征提取和模式識別，該方法能自適應(yīng)分解原始故障信號，，提取出故障特征后輸入至分類器進(jìn)行故障識別便可實現(xiàn)多故障識別與診斷。通過實驗數(shù)據(jù)分析，并與經(jīng)典HMM的對比實驗，說明了該方法的有效性。
[Abstract]:In the process of fault diagnosis, fault signal feature extraction and fault pattern recognition are the key steps in the whole process. Based on this, a new time-frequency analysis method, Local mean decomposition (LMDM), and another statistically based pattern recognition technique, Hidden Markov Model (HMMMMM), are applied to fault diagnosis of rotor systems. In this paper, the common faults of the rotor test-bed are simulated. In order to improve the information of feature extraction, the LMD-based method is used to extract the fault signal. At the same time, the hmm method, which is very rigorous in statistical theory, is used for fault pattern recognition. According to the above theory and algorithm, the main work and conclusions of this paper are as follows: 1) four typical rotor fault vibration signals are simulated on the test bed. The vibration mechanism and process of fault signal are deeply studied, and wavelet filtering and spectrum analysis are carried out. Experimental results show that the signal obtained by wavelet de-noising is smooth, and the frequency components of the spectrum are very obvious, which is suitable for the data processing of rotor system. A new fault feature extraction method based on LMD and approximate entropy is proposed. The sum of function and a surplus, Then the approximate entropy of each PF is calculated as the fault eigenvalue and all the fault eigenvalues are combined into eigenvector sets by using the improved fast approximate entropy calculation method. The experimental results show that the fault eigenvector set based on LMD and approximate entropy can accurately reflect the fault signal features, and is suitable for fault classification with hmm classifier. Aiming at the problem of relatively low probability of finding global best for hmm, a pattern classifier for hmm optimized by particle swarm optimization (PSO) is proposed. This method can optimize hmm training model to find the global optimal probability accurately. The amplitude extracted from the gearbox fault data is input into the classifier as the eigenvector to classify the data. The experimental results show that the convergence error is small. The fault diagnosis method based on LMD approximation entropy and PSO optimization is proposed to extract fault features and recognize the fault signals collected on the rotor test-bed. This method can decompose the original fault signal adaptively, extract the fault feature and input it to the classifier for fault identification, and then realize the multi-fault identification and diagnosis. The effectiveness of the proposed method is demonstrated by analyzing the experimental data and comparing it with the classical hmm.
【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 郭鋼祥;基于局域均值分解和神經(jīng)網(wǎng)絡(luò)的柴油機(jī)故障診斷研究[D];中北大學(xué);2013年

本文編號：2046623