發(fā)布時(shí)間：2018-04-21 23:39

  本文選題：轉(zhuǎn)子 + 聚類�。� 參考：《中國(guó)工程機(jī)械學(xué)報(bào)》2017年02期

【摘要】：對(duì)信號(hào)進(jìn)行特征提取是故障診斷的關(guān)鍵,為了提高轉(zhuǎn)子振動(dòng)故障診斷的準(zhǔn)確性,根據(jù)轉(zhuǎn)子振動(dòng)的特點(diǎn)提出了基于振幅熵H(A)與功率譜重心C的轉(zhuǎn)子振動(dòng)故障診斷方法.通過(guò)計(jì)算功率譜的重心得到表征功率譜變化的功率譜重心特征,計(jì)算振幅的熵值得到反映幅值分布特征與振動(dòng)集中程度的振幅熵特征,組成二維特征量(H(A),C).然后通過(guò)轉(zhuǎn)子故障模擬實(shí)驗(yàn)采集數(shù)據(jù),對(duì)其進(jìn)行DBSCAN聚類、K均值聚類、層次聚類、網(wǎng)格聚類4種聚類分析.結(jié)果表明,基于振幅熵H(A)與功率譜重心C的二維特征量(H(A),C)能夠作為評(píng)價(jià)轉(zhuǎn)子振動(dòng)狀態(tài)的綜合特征指標(biāo).通過(guò)對(duì)傳統(tǒng)的二維特征量(偏度、均方根值)、(裕度、標(biāo)準(zhǔn)差)運(yùn)用網(wǎng)格聚類法進(jìn)行轉(zhuǎn)子振動(dòng)故障診斷識(shí)別,結(jié)果表明,(H(A),C)的選取較于傳統(tǒng)特征量的選取能更好地對(duì)轉(zhuǎn)子運(yùn)行中出現(xiàn)的常見(jiàn)故障進(jìn)行區(qū)分.
[Abstract]:Feature extraction is the key of fault diagnosis. In order to improve the accuracy of rotor vibration fault diagnosis, a rotor vibration fault diagnosis method based on amplitude entropy (HPA) and power spectrum center of gravity (C) is proposed. By calculating the center of gravity of the power spectrum, the barycenter characteristic of the power spectrum is obtained. The entropy of the calculated amplitude is worth the amplitude entropy characteristic reflecting the distribution of amplitude and the degree of vibration concentration. Then, through the rotor fault simulation experiment to collect data, DBSCAN clustering K-means clustering, hierarchical clustering, grid clustering four kinds of clustering analysis. The results show that the 2-D eigenvalue of the power spectrum center of gravity C based on the amplitude entropy (H) and the power spectral center C (C) can be used as a comprehensive characteristic index to evaluate the vibration state of the rotor. By using the grid clustering method to identify the rotor vibration fault diagnosis, the traditional two-dimensional characteristic variables (bias, root mean square value) (margin, standard deviation) are used to diagnose and identify the rotor vibration fault. The results show that compared with the traditional feature selection, the selection of the HGV / C) can better distinguish the common faults in the rotor operation.
【作者單位】： 南昌航空大學(xué)航空制造工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(51365040) 航空科學(xué)基金資助項(xiàng)目(2013ZD56009) 江西省自然科學(xué)基金資助項(xiàng)目(20151BAB206060) 江西省研究生創(chuàng)新專項(xiàng)資金資助項(xiàng)目(YC2015-S314)
【分類號(hào)】：TH17

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