Mechanical Fault Diagnosis of Fan Based on Wavelet Packet Energy Analysis and Improved Support Vector Machine

Journal of Chinese Society of Power Engineering ›› 2013, Vol. 33 ›› Issue (8) : 606-612.

Automatical Controlling and Detecting Diagnosis

XU Xiaogang, WANG Songling, LIU Jinlian

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Abstract

To accurately diagnose the mechanical faults of fan, a new method was proposed based on wavelet packet energy analysis and improved support vector machine. Vibration signals of the fan were acquired on a 4-73 No.8D test bench under 13 different operating conditions, which were subsequently denoised, decomposed and reconstructed by wavelet packet to extract and obtain multipoint information fusion wavelet packet energy eigenvectors under various operating conditions. The sample set of above eigenvectors was trained and tested by improved support vector machine so as to diagnose and classify the mechanical faults of the fan. Results show that this method is able to effectively diagnose the category, severity and site of the fan mechanical faults with high diagnostic accuracy rate, short testing time and good online diagnosis performance.

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fan / fault diagnosis / wavelet packet energy analysis / support vector machine / optimization

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XU Xiaogang, WANG Songling, LIU Jinlian. Mechanical Fault Diagnosis of Fan Based on Wavelet Packet Energy Analysis and Improved Support Vector Machine. Journal of Chinese Society of Power Engineering. 2013, 33(8): 606-612

References

[1] 牛培峰,张君.小波分析技术在汽轮机故障诊断中的应用[J].动力工程,2007,27(1):76-80.
NIU Peifeng,ZHANG Jun.Application of the wavelet transform technique in turbo-generator fault diagnosis[J].Journal of Power Engineering,2007,27(1):76-80.
[2] LI K,CHEN P,WANG H Q.Intelligent diagnosis method for rotating machinery using wavelet transform and ant colony optimization[J].Sensors Journal,2012,12(7):2474-2484.
[3] WANG Wenyi.Early detection of gear tooth cracking using the resonance demodulation technique[J].Mechanical Systems and Signal Processing,2001,15(5):887-903.
[4] NIKOLAOU N G,ANTONIADIS I A.Rolling element bearing fault diagnosis using wavelet packets[J].NDT & E International,2002,35(3):197-205.
[5] 梁平,白蕾,龙新峰,等.基于小波包分析及神经网络的汽轮机转子振动故障诊断[J].控制理论与应用,2007,24(6):981-985.
LIANG Ping,BAI Lei,LONG Xinfeng,et al.Turbine rotor vibration faults diagnosis based on wavelet packet analysis and neural network[J].Control Theory & Applications,2007,24(6):981-985.
[6] 韩磊,洪杰,王冬.基于小波包分析的航空发动机轴承故障诊断[J].推进技术,2009,30(3):328-332.
HAN Lei,HONG Jie,WANG Dong.Fault diagnosis of aero-engine bearings based on wavelet package analysis[J].Journal of Propulsion Technology,2009,30(3):328-332.
[7] 彭文季,罗兴錡.基于小波包分析和支持向量机的水电机组振动故障诊断研究[J].中国电机工程学报,2006,26(24):164-168.
PENG Wenji,LUO Xingqi.Research on vibrant fault diagnosis of hydro-turbine generating unit based on wavelet packet analysis and support vector machine[J].Proceedings of the CSEE,2006,26(24):164-168.
[8] 郑蕊蕊,赵继印,赵婷婷,等.基于遗传支持向量机和灰色人工免疫算法的电力变压器故障诊断[J].中国电机工程学报,2011,31(7):56-63.
ZHENG Ruirui,ZHAO Jiyin,ZHAO Tingting,et al.Power transformer fault diagnosis based on genetic support vector machine and gray artificial immune algorithm[J].Proceedings of the CSEE,2011,31(7):56-63.
[9] 白卫东,严建华,马增益,等.基于支持向量机的火焰状态识别方法[J].动力工程,2004,24(4):548-551.
BAI Weidong,YAN Jianhua,MA Zengyi,et al.Method of flame identification based on support vector machine[J].Power Engineering,2004,24(4):548-551.
[10] 刘永斌,何清波,孔凡让,等.基于PCA和SVM的内燃机故障诊断[J].振动、测试与诊断,2012,32(2):250-255.
LIU Yongbin,HE Qingbo,KONG Fanrang,et al.Fault diagnosis of internal combustion engine using PCA and SVM[J].Journal of Vibration, Measurement & Diagnosis,2012,32(2):250-255.
[11] 杨宏晖,陈兆基,戴键.基于自适应增强SVM集成算法的风机故障诊断[J].测控技术,2010,29(7):72-74.
YANG Honghui,CHEN Zhaoji,DAI Jian.Fault diagnosis for fan based on self-adaptive boosting SVM ensemble[J].Measurement & Control Technology,2010,29(7):72-74.
[12] 闫志刚,杜培军.多类支持向量机推广性能分析[J].采集与处理,2009,24(4):469-475.
YAN Zhigang,DU Peijun.Generalization performance analysis of M-SVMs[J].Journal of Data Acquisition & Processing,2009,24(4):469-475.
[13] 石志标,宋全刚,马明钊,等.基于改进粒子群优化支持向量机的汽轮机组故障诊断[J].动力工程学报,2012,32(6):454-457.
SHI Zhibiao,SONG Quangang,MA Mingzhao,et al.Fault diagnosis of steam turbine based on MPSO-SVM algorithm[J].Journal of Chinese Society of Power Engineering,2012,32(6):454-457.
[14] 吴明圣.径向基神经网络和支持向量机的参数优化方法研究及应用[D].长沙:中南大学,2007.
[15] 张楠.关于支持向量机中的参数优化的研究[D].西安:西北大学,2008.

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Received	Published
2012-12-20	2013-08-25
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2013-08-25

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