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基于正则化多核判别分析的航空发动机滚动轴承早期故障融合诊断方法

郝腾飞 陈果 廖仲坤 程小勇 赵斌 王海飞

郝腾飞, 陈果, 廖仲坤, 程小勇, 赵斌, 王海飞. 基于正则化多核判别分析的航空发动机滚动轴承早期故障融合诊断方法[J]. 航空动力学报, 2013, 28(12): 2759-2770.
引用本文: 郝腾飞, 陈果, 廖仲坤, 程小勇, 赵斌, 王海飞. 基于正则化多核判别分析的航空发动机滚动轴承早期故障融合诊断方法[J]. 航空动力学报, 2013, 28(12): 2759-2770.
HAO Teng-fei, CHEN Guo, LIAO Zhong-kun, CHENG Xiao-yong, ZHAO Bin, WANG Hai-fei. Approach for incipient fusion fault diagnosis of rolling bearing of aero-engine based on regularized multiple kernel discriminant analysis[J]. Journal of Aerospace Power, 2013, 28(12): 2759-2770.
Citation: HAO Teng-fei, CHEN Guo, LIAO Zhong-kun, CHENG Xiao-yong, ZHAO Bin, WANG Hai-fei. Approach for incipient fusion fault diagnosis of rolling bearing of aero-engine based on regularized multiple kernel discriminant analysis[J]. Journal of Aerospace Power, 2013, 28(12): 2759-2770.

基于正则化多核判别分析的航空发动机滚动轴承早期故障融合诊断方法

基金项目: 国家自然科学基金(61179057)

Approach for incipient fusion fault diagnosis of rolling bearing of aero-engine based on regularized multiple kernel discriminant analysis

  • 摘要: 针对基于机匣测点信号的航空发动机滚动轴承早期故障诊断问题,提出了一种基于正则化多核判别分析的融合诊断方法.该方法首先提取多种类型的滚动轴承故障特征;然后采用相同的一组核参数为不同类型的特征分别构造一组核矩阵,并将所有核矩阵组合在一起;最后通过求解一个半无限线性规划得到该组核矩阵关于正则化核判别分析的目标函数的最优线性组合系数,进一步采用该系数计算所有核矩阵的线性组合,从而实现多种类型特征信息的融合.实验结果表明:该方法诊断正确率与采用单一类型特征诊断的最高正确率相比提高了9.25%,同时可以避免核矩阵需要人工选择的问题,从而进一步提高了故障诊断的自动化水平.

     

  • [1] 翟旭升, 胡金海, 谢寿生, 等.基于DSmT的航空发动机早期振动故障融合诊断方法[J].航空动力学报, 2012, 27(1):1-6. ZHAI Xusheng, HU Jinhai, XIE Shousheng, et al.Diagnosis of aero-engine with early vibration fault symptom using DSmT[J].Journal of Aerospace Power, 2012, 27(1):1-6.(in Chinese)
    [2] 易良榘.简易振动诊断现场实用技术[M].北京:机械工业出版社, 2003.
    [3] 陈果.滚动轴承早期故障的特征提取与智能诊断[J].航空学报, 2009, 30(2):362-367. CHEN Guo.Feature extraction and intelligent diagnosis for ball bearing early faults[J].Acta Aeronautica et Astronautica Sinica, 2009, 30(2):362-367.(in Chinese)
    [4] 杨宇, 王欢欢, 程军圣, 等.基于LMD的包络谱特征值在滚动轴承故障诊断中的应用[J].航空动力学报, 2012, 27(5):1153-1158. YANG Yu, WANG Huanhuan, CHENG Junsheng, et al.Application of envelope spectrum characteristics based on LMD to roller bearing fault diagnosis[J].Journal of Aerospace Power, 2012, 27(5):1153-1158.(in Chinese)
    [5] YANG Yu, YU Dejie, CHENG Junsheng.A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J].Journal of Sound and Vibration, 2006, 294(1):269-277.
    [6] Huang N E, Shen Z, Long S R, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London Series A:Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995.
    [7] 杨宇, 于德介, 程军圣.基于EMD的奇异值分解技术在滚动轴承故障诊断中的应用[J].振动与冲击, 2005, 24(2):12-16. YANG Yu, YU Dejie, CHENG Junsheng.Application of EMD based singular value decomposition technique to fault diagnosis for roller bearing[J].Journal of Vibration and Shock, 2005, 24(2):12-16.(in Chinese)
    [8] 程军圣, 于德介, 杨宇.基于EMD和SVM的滚动轴承故障诊断方法[J].航空动力学报, 2006, 21(3):575-580. CHENG Junsheng, YU Dejie, YANG Yu.Fault diagnosis of roller bearing based on EMD and SVM[J].Journal of Aerospace Power, 2006, 21(3):575-580.(in Chinese)
    [9] 王太勇, 何慧龙, 王国锋, 等.基于经验模式分解和最小二乘支持矢量机的滚动轴承故障诊断[J].机械工程学报, 2007, 43(4):88-92. WANG Taiyong, HE Huilong, WANG Guofeng, et al.Rolling-bearings fault diagnosis based on empirical mode decomposition and least square support vector machine[J].Chinese Journal of Mechanical Engineering, 2007, 43(4):88-92.(in Chinese)
    [10] Muller K R, Mika S, Ratsch G, et al.An introduction to kernel-based learning algorithms[J].IEEE Transactions on Neural Networks, 2001, 12(2):181-201.
    [11] HU Qiao, HE Zhengjia, ZHANG Zhousuo, et al.Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble[J].Mechanical Systems and Signal Processing, 2007, 21(2):688-705.
    [12] 陶新民, 杜宝祥, 徐勇.基于HOS奇异值谱的SVDD轴承故障检测方法[J].振动工程学报, 2008, 21(2):203-208. TAO Xinmin, DU Baoxiang, XU Yong.Bearing fault detection using SVDD based on HOS-singular value spectrum[J].Journal of Vibration Engineering, 2008, 21(2):203-208.(in Chinese)
    [13] 孙超英, 刘鲁, 刘传武, 等.基于Boosting-SVM算法的航空发动机故障诊断[J].航空动力学报, 2010, 25(11):2584-2588. SUN Chaoying, LIU Lu, LIU Chuanwu, et al.Aero-engine fault diagnosis based on boosting-SVM[J].Journal of Aerospace Power, 2010, 25(11):2584-2588.(in Chinese)
    [14] 卢艳辉, 尹泽勇.基于小波包分析方法的航空发动机滚动轴承故障诊断[J].燃气涡轮试验与研究, 2005, 18(1):35 -38. LU Yanhui, YIN Zeyong.Fault diagnosis of aero-engine rolling element bearing based on the wavelet packet analysis[J].Gas Turbine Experiment and Research, 2005, 18(1):35-38.(in Chinese)
    [15] 陈果.基于神经网络和D-S证据理论的发动机磨损故障融合诊断[J].航空动力学报, 2005, 20(2):303-308. CHEN Guo.Fusion diagnosis of engine wearing fault based on neural networks and D-S evidence theory[J]. Journal of Aer楯湳杰獡?潥映?瑯桷敥?水?琲栰‰?測琠攲爰渨愲琩椺漳渰愳氭″?漸渮昨敩牮攠湃捨敩?潥湳??愼换桲椾湛攱?汝攠慨狺湳椬渠柄?亟攬眠?夺漬爠歉??喎叁?????????ラ??丁??????????抨犛?宥水?崲‰到愸欬漠琲漳洨愱洲漩渺樲礳′??′?愳挱栮???剎???慩湡畮?卩??攬琠?慕汁?升椠浈灯汮敧????孧?崠??潁畏爠湑慩污?潧昬??慴挠桡楬渮敄??敧慮牯湳楩湳朠?剥整獨敯慤爠捯桦???ひは??????ㄠ?????ㄠ???????扡牳?孤㈠?嵮??楨湥?奄???楳畴?呲?????略桲???卩??畮汣瑥椠灴汨敥?歲敹牛湊敝氮?汯敵慲牮湡楬渠杯?映潁牥?摯楳浰敡湣獥椠潐湯慷汥楲琬礠′爰攰搸甬挠琲椳漨渱嬲?崺?????′吳爳愱渮猨慩据琠楃潨湩獮?潳湥?值慢瑲琾敛爱渷??湈懋波礬猠榋獰?愬渠摈??愪捺棑榨渺旑??済瓑斧氭沄槨束故溜掍攈???ぶ???????????ㄠ?????????戱爩?嬱财?崭??椵洮?千?????慩杢湯愬渠楓?????潡祮摱?匬?佃灈瑅楎洠慇汵?欮敓牴湵敤汹?獯敮氠敦捵瑳楩潯湮?楤湩?歧敮牯湳敩汳?晴楥獣桨敮物?摵楥獳挠牯楦洠楷湥慡湲琠?慡湵慬汴祳猠楩獮嬠?嵹??偨牥潳捩敺敥摤椠湭杯獮?潴景?瑩桮敧???爠摡??湯琭敥牮湧慩瑮楥潛湊慝氮??潵湲普敡牬攠湯捦攠?潥湲??慰捡档楥渠敐?汷敥慲爬渠椲渰朰?丬攠眲?夨漱爩欺??唹匭???????㈠ぃと???????????戱爸?嬠二?崬?姄???椠效灜椮渪杺?????匧棽畅榜瞍愈滊枭?????乊??椪慺溨梛畦榥??甲氰琰椹?挠氲愴猨猷?携椱猶挴爹椭洱椶渵愳渮琠?歕攠牆湥敮汧?氠效慕牁湎楇渠杊?癮楱慵?据漬渠癃效硅?瀠牙潵朮牒慥浳浥楡湲杣孨?嵯??潰略牲湦慯汲?潡普??愠捦桡極湬整??敵慳物湯楮渠杤?剡敧獮敯慳物捳栠??㈠ちづ?????????ㄠ???????扮牴?孊??嵊?婵??乡??婯桦椠桁略慲???????畐慯湷来??堠唲‰?漹測朠昲甴??攩琺?愶水?刭攱朶电氳愮爨楩穮攠摃?摩楮獥捳牥椩洼楢湲愾湛琱?慝渠憋泤礸猬椠猐??爠榍搠枎攬?牉攮朡狊斨猆玌槁潮渆?憍済撄?扺旑禨漺湅撜寊?嵛??漮甪牺溨憛汦?漬映′?愱挱栬椠渲收??攩愺爲渱椰渱札′刱攰猶攮愠牗捁桎???ふ?と???????????????水???扏爠?孨?ち嵮??????整渠条?????塧楮慯潳晩敳椠????乯??楯慦眠敡楥?卯瀭敥敮摧?畮灥?武敡牵湬整氠?摡楳獥捤爠楯浮椠湦慵湺瑺?愠湩慮汦祥獲楥獮季?崠?呮桤攠?噶?????潥甠牴湨慥汯???お?????ひ??????????ospace Power, 2011, 26(9):2101-2106.(in Chinese)
    [16] 赵世荣, 黄向华.应用神经网络信息融合诊断航空发动机故障[J].航空动力学报, 2008, 23(1):163-168. ZHAO Shirong, HUANG Xianghua.Fault diagnosis for aeroengine gas path components based on neural network multisensory data fusion[J].Journal of Aerospace Power, 2008, 23(1):163-168.(in Chinese)
    [17] 曲建岭, 唐昌盛, 肖辉雄, 等.人工神经网络融合诊断航空发动机气路故障[J].航空动力学报, 2008, 23(11):2124-2127. QU Jianling, TANG Changsheng, XIAO Huixiong, et al.Integrated diagnosis of aeroengines' gas path faults using artificial neural network[J].Journal of Aerospace Power, 2008, 23(11):2124-2127.(in Chinese)
    [18] Lanckriet G R G, Cristianini N, Bartlett P, et al.Learning the kernel matrix with semidefinite programming[J].Journal of Machine Learning Research, 2004, 5(1):27-72.
    [19] Sonnenburg S, Ratsch G, Schafer C, et al.Large scale multiple kernel learning[J].Journal of Machine Learning Research, 2006, 7(7):1531-1565.
    [20] Zien A, Ong C S.Multiclass multiple kernel learning[C]//Proceed
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  • 收稿日期:  2012-11-12
  • 刊出日期:  2013-12-28

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