本文選題：譜峭度 + 可調(diào)Q因子小波變換　； 參考：《武漢科技大學》2016年博士論文

【摘要】：機械設備是現(xiàn)代化生產(chǎn)的重要工具,對其開展運行狀態(tài)監(jiān)測及故障診斷對于保障安全化生產(chǎn)具有重要意義。識別早期故障征兆并提取故障特征,是機械設備狀態(tài)監(jiān)測及故障診斷的關鍵。近年來快速發(fā)展的信號稀疏表示理論,為基于振動分析的早期故障特征提取及診斷提供了強有力的工具。本論文以國家自然科學基金項目“低速重載機械早期故障稀疏特征識別的研究”為依托,以信號稀疏表示為主要理論工具,圍繞機械設備易發(fā)生故障部件——軸承和齒輪的早期故障特征提取及診斷開展了深入研究。主要研究內(nèi)容如下:1.針對早期故障特征易被噪聲覆蓋而難以準確提取的問題,提出了基于可調(diào)Q因子小波變換的早期故障特征提取方法。該方法先利用可調(diào)Q因子小波變換對設備的振動信號在不同的Q因子和尺度下分解,以峭度值最大原則確定最佳的Q因子和尺度帶,再利用相鄰系數(shù)降噪方法處理尺度帶內(nèi)的變換系數(shù),最后通過小波逆變換提取故障特征。實驗研究表明,該方法可有效提取設備在中速運轉(zhuǎn)下的早期故障特征,相比于傳統(tǒng)小波方法,其提取結(jié)果噪聲更小,包絡譜圖上故障特征頻率更突出。2.針對利用稀疏表示原理對早期故障特征提取時、準確匹配特征成分的字典難以構(gòu)造的問題,提出了基于字典學習的早期故障稀疏特征提取方法。該方法以故障信號與正常信號的差值為訓練信號,利用改進型K均值奇異值分解字典學習算法構(gòu)造匹配特征成分的字典;在稀疏分解過程中,通過計算每次迭代后逼近信號的峭度值,找出峭度值最大時對應的逼近信號,自適應確定特征成分與噪聲成分的稀疏分解分界點。對比實驗結(jié)果表明,該方法可有效提取設備在低速運轉(zhuǎn)下的早期故障特征,相比于參數(shù)化的字典,其提取結(jié)果具有更高的精度。3.針對故障部件參數(shù)未知的單一故障診斷問題,提出了基于組稀疏分類的故障診斷方法。該方法先將已知故障類型的訓練樣本和未知故障類型的待測樣本轉(zhuǎn)換至頻域,利用訓練樣本的頻域系數(shù)組合成稀疏分解的字典,再將待測樣本的頻域系數(shù)在該字典上進行組稀疏分解,最后根據(jù)各組重構(gòu)誤差的最小值所在的類別確定故障類型。通過故障實驗測試,驗證了該方法在理論特征頻率的未知情況下,可準確診斷出滾動軸承和齒輪的單一故障類型。4.針對故障部件參數(shù)未知的復合故障診斷問題,提出了基于小波包系數(shù)稀疏分類的故障診斷方法。該方法先對已知各單一故障類型的訓練樣本進行小波包變換,憑借距離評價參數(shù)篩選出具有類別差異的頻帶,并利用這些頻帶內(nèi)的小波包系數(shù)構(gòu)造稀疏分解的字典組,再將待測復合故障類型的測試樣本小波包頻帶系數(shù)在對應字典上稀疏分解,通過各組稀疏重構(gòu)誤差最小值所在類別逐一判斷復合故障類型。軸承和齒輪的復合故障診斷實驗結(jié)果驗證了該方法的有效性。
[Abstract]:Mechanical equipment is an important tool in modern production. It is of great significance to carry out operation state monitoring and fault diagnosis to ensure safe production. Identifying early fault signs and extracting fault features are the key to condition monitoring and fault diagnosis of machinery and equipment. The rapid development of signal sparse representation theory in recent years provides a powerful tool for early fault feature extraction and diagnosis based on vibration analysis. This thesis is based on the project of National Natural Science Foundation of China "Research on early Fault sparse feature recognition of low Speed heavy haul Machinery", and the signal sparse representation is the main theoretical tool. The early fault feature extraction and diagnosis of bearing and gear are studied in detail. The main research contents are as follows: 1. In order to solve the problem that early fault features are easily covered by noise and difficult to extract accurately, an early fault feature extraction method based on Q-adjustable factor wavelet transform is proposed. In this method, the vibration signal of the equipment is decomposed under different Q factors and scales by using the adjustable Q factor wavelet transform, and the best Q factor and scale band are determined by the principle of maximum kurtosis. The transform coefficients in the scale band are processed by the adjacent coefficient denoising method, and the fault features are extracted by the inverse wavelet transform. The experimental results show that this method can effectively extract the early fault features of the equipment under medium speed operation. Compared with the traditional wavelet method, the result of the method is less noise and the frequency of fault feature on the envelope spectrum is more prominent. 2. In order to solve the problem that it is difficult to construct a dictionary that can accurately match the feature components in early fault feature extraction using sparse representation principle, a dictionary learning based method for early fault sparse feature extraction is proposed. Using the difference between the fault signal and the normal signal as the training signal, the improved K-means singular value decomposition dictionary learning algorithm is used to construct a dictionary that matches the characteristic components. By calculating the kurtosis value of the approximate signal after each iteration, the approximate signal corresponding to the maximum kurtosis value is found, and the sparse decomposition boundary point of the characteristic component and the noise component is determined adaptively. The experimental results show that this method can effectively extract the early fault features of the equipment at low speed. Compared with the parameterized dictionary, the method has a higher precision of .3. To solve the problem of single fault diagnosis with unknown parameters of fault components, a fault diagnosis method based on group sparse classification is proposed. In this method, the training samples of the known fault type and the unknown fault type samples are converted to the frequency domain, and the frequency-domain coefficients of the training samples are combined into a sparse decomposed dictionary. Then the frequency domain coefficients of the samples to be tested are decomposed in the dictionary and the fault type is determined according to the category of the minimum reconstruction error of each group. Through the fault test, it is proved that the method can accurately diagnose the single fault type of rolling bearing and gear in the case of unknown theoretical characteristic frequency. A fault diagnosis method based on sparse classification of wavelet packet coefficients is proposed for composite fault diagnosis with unknown parameters of fault components. In this method, the training samples of each known single fault type are first transformed by wavelet packet transform, and the frequency bands with different categories are screened by the distance evaluation parameters, and the sparse decomposition dictionary group is constructed by using the wavelet packet coefficients in these bands. Then the wavelet packet frequency band coefficients of the test samples are decomposed in the corresponding dictionaries, and the types of composite faults are judged by the categories in which the minimum error of each group of sparse reconstruction is located. The experimental results of bearing and gear fault diagnosis show that the method is effective.
【學位授予單位】：武漢科技大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TH17

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