【摘要】：齒輪故障診斷中關(guān)鍵的兩個部分就是信號特征的提取和模式識別。本課題綜合分析了國內(nèi)外對齒輪故障診斷技術(shù)的研究現(xiàn)狀，在齒輪的振動機(jī)理及齒輪故障的振動信號特征的基礎(chǔ)上，提出了一種多維多尺度特征提取算法和一種基于模糊C聚類的故障分類算法。具體的研究內(nèi)容如下： (1)在總結(jié)了齒輪不同狀態(tài)下的振動信號特征的基礎(chǔ)上，采用仿真信號，模擬了齒輪正常、磨損和斷齒的振動信號特征，更為直觀地掌握了不同故障的振動信號特征，同時也為故障分類提供了仿真信號。 (2)提出了一種多維多尺度特征提取算法。待處理信號首先經(jīng)過奇異值分解，將一維信號重構(gòu)成多維信號，凸顯出更多的特征信息；然后采用奇異值分解，分解成不同尺度的調(diào)幅——調(diào)頻信號，取能量高的乘積函數(shù)求和重構(gòu)；最后，，利用形態(tài)差值濾波器提取出特征信息，通過仿真實驗和齒輪故障模擬實驗驗證了該方法的有效性。 (3)在特征提取的基礎(chǔ)上，將模糊C聚類算法引入到故障分類中，從提取的特征信息中選擇合適的特征量，通過仿真實驗和齒輪故障模擬實驗驗證了模糊C聚類算法是一種有效的故障分類算法。 本課題提出了一種有效的故障特征提取算法和故障分類算法，也驗證其有效性，為齒輪故障診斷提供一種有效、準(zhǔn)確的方法。
[Abstract]:Two key parts in gear fault diagnosis are signal feature extraction and pattern recognition. This paper comprehensively analyzes the research status of gear fault diagnosis technology at home and abroad, based on the vibration mechanism of gear and the characteristics of vibration signal of gear fault. A multi-dimensional and multi-scale feature extraction algorithm and a fault classification algorithm based on fuzzy C clustering are proposed. The specific research contents are as follows: (1) on the basis of summarizing the vibration signal characteristics of gear under different states, the vibration signal characteristics of gear normal, wear and broken teeth are simulated by using simulation signal. The vibration signal characteristics of different faults are grasped more intuitively, and the simulation signals are also provided for fault classification. (2) A multi-dimensional and multi-scale feature extraction algorithm is proposed. After singular value decomposition (SVD), the one-dimensional signal is reconstituted into multi-dimensional signal, which highlights more characteristic information. Then singular value decomposition (SVD) is used to decompose the amplitude modulation-FM signal of different scales, and the product function of high energy is taken to sum and reconstruct. Finally, the morphological difference filter is used to extract the feature information, and the effectiveness of the method is verified by simulation experiment and gear fault simulation experiment. (3) on the basis of feature extraction, fuzzy C clustering algorithm is introduced into fault classification, and the appropriate feature quantity is selected from the extracted feature information. The fuzzy C clustering algorithm is proved to be an effective fault classification algorithm by means of simulation experiments and gear fault simulation experiments. In this paper, an effective fault feature extraction algorithm and fault classification algorithm are proposed, and the validity of the algorithm is verified, which provides an effective and accurate method for gear fault diagnosis.
【學(xué)位授予單位】：武漢科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TH165.3

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