应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断

Abstract

针对固有时间尺度分解算法的模态混叠问题和最小二乘支持向量机的参数优化问题，本文提出了一种新的基于完备集合固有时间尺度分解和混合差分进化和粒子群算法优化最小二乘支持向量机的柴油机故障诊断方法。该方法主要包括以下几个步骤：首先，为解决固有时间尺度分解算法的模态混叠问题，提出了一种完备集合固有时间尺度分解算法。随后，利用完备集合固有时间尺度分解算法将非平稳的柴油机振动信号分解为一系列平稳的旋转分量和残差信号。然后，提取了前几阶旋转分量的三类典型的时频特征，包括奇异值、旋转分量能量和能量熵、AR模型参数，作为故障特征。最后，提出了混合差分进化和粒子群算法对最小二乘支持向量机的参数进行优化的方法，并通过将故障特征输入训练好的最小二乘支持向量机模型实现故障诊断。仿真和实验结果表明提出的故障诊断方法可以克服固有时间尺度分解的模态混叠问题，而且能够准确的识别柴油机故障。

Keywords

柴油机 / 故障诊断 / 完备集合固有时间尺度分解 / 最小二乘支持向量机 / 混合差分进化和粒子群优化算法

References

[1] Ardia, D., Boudt, K., Carl, P., , 2011. Differential evolu-tion with DEoptim: an application to non-convex portfo-lio optimization. R J., 3(1):27–34.

[2] Chen, B.J., He, Z.J., Chen, X.F., , 2011. A demodulating approach based on local mean decomposition and its ap-plications in mechanical fault diagnosis. Meas. Sci. Technol., 22(5):055704. http://dx.doi.org/10.1088/0957-0233/22/5/055704

[3] Chen, M., Zheng, A.X., Jordan, M.I., , 2004. Failure diagnosis using decision trees. Int. Conf. on Autonomic Computing, p.36–43. http://dx.doi.org/10.1109/ICAC.2004.31

[4] Cheng, J.S., Yu, D.J., Yang, Y., 2006. A fault diagnosis ap-proach for roller bearings based on EMD method and AR model. Mech. Syst. Signal Process., 20(2):350–362. http://dx.doi.org/10.1016/j.ymssp.2004.11.002

[5] Cheng, J.S., Zheng, J.D., Yang, Y., 2012. A nonstationary signal analysis approach: the local characteristic-scale decomposition method. J. Vibr. Eng., 25(2):215–220 (in Chinese). http://dx.doi.org/10.3969/j.issn.1004-4523.2012.02.017

[6] Cheng, M.Y., Hoang, N.D., Wu, Y.W., 2013. Hybrid intelli-gence approach based on LS-SVM and differential evo-lution for construction cost index estimation: a Taiwan case study. Autom. Constr., 35:306–313. http://dx.doi.org/10.1016/j.autcon.2013.05.018

[7] Eberhart, R.C., Kennedy, J., 1995. A new optimizer using particle swarm theory. Proc. 6th Int. Symp. on Micro Machine and Human Science, p.39–43. http://dx.doi.org/10.1109/MHS.1995.494215

[8] Frei, M.G., Osorio, I., 2007. Intrinsic time-scale decomposition: time-frequency-energy analysis and real-time filtering of non-stationary signals. Proc. R. Soc. A, 463(2078): 321–342. http://dx.doi.org/10.1098/rspa.2006.1761

[9] Hong, H., Wang, X.L., Tao, Z.Y., , 2011. Centroid-based sifting for empirical mode decomposition. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 12(2):88–95. http://dx.doi.org/10.1631/jzus.C1000037

[10] Huang, J., Hu, X., Geng, X., 2011. An intelligent fault diag-nosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine. Electr. Power Syst. Res., 81(2):400–407. http://dx.doi.org/10.1016/j.epsr.2010.10.029

[11] Huang, N.E., Shen, Z., Long, S.R., , 1998. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc. R. Soc. A, 454(1971):903–995. http://dx.doi.org/10.1098/rspa.1998.0193

[12] Huang, W., Kong, F., Zhao, X., 2015. Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory. J. Intell. Manuf., in press. http://dx.doi.org/10.1007/s10845-015-1174-x

[13] Jiang, X., Li, S., Wang, Y., 2015. A novel method for self-adaptive feature extraction using scaling crossover characteristics of signals and combining with LS-SVM for multi-fault diagnosis of gearbox. J. Vibroeng., 17(4): 1861–1878.

[14] Kadambe, S., Boudreaux-Bartels, G.F., 1992. A comparison of the existence of ‘cross terms’ in the Wigner distribution and the squared magnitude of the wavelet transform and the short-time Fourier transform. IEEE Trans. Signal Process., 40(10):2498–2517. http://dx.doi.org/10.1109/78.157292

[15] Lei, Y.G., He, Z.J., Zi, Y.Y., , 2007. Fault diagnosis of rotating machinery based on multiple ANFIS combina-tion with GAs. Mech. Syst. Signal Process., 21(5):2280–2294. http://dx.doi.org/10.1016/j.ymssp.2006.11.003

[16] Lei, Y.G., He, Z.J., Zi, Y.Y., 2009. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech. Syst. Signal Process., 23(4):1327–1338. http://dx.doi.org/10.1016/j.ymssp.2008.11.005

[17] Li, J., Li, S., Chen, X., , 2015. The hybrid KICA-GDA- LSSVM method research on rolling bearing fault feature extraction and classification. Shock Vibr., 2015:1–9. http://dx.doi.org/10.1155/2015/512163

[18] Li, Y., Tse, P.W., Yang, X., , 2010. EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine. Mech. Syst. Signal Pro-cess., 24(1):193–210. http://dx.doi.org/10.1016/j.ymssp.2009.06.012

[19] Li, Z., Yan, X., Yuan, C., , 2011. Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method. Mech. Syst. Signal Process., 25(7): 2589–2607. http://dx.doi.org/10.1016/j.ymssp.2011.02.017

[20] Lin, J.S., 2012. Improved intrinsic time-scale decomposition method and its simulation. Appl. Mech. Mater., 121-126: 2045–2048. http://dx.doi.org/10.4028/www.scientific.net/ AMM.121-126.2045

[21] Mallipeddi, R., Suganthan, P.N., Pan, Q.K., , 2011. Dif-ferential evolution algorithm with ensemble of parame-ters and mutation strategies. Appl. Soft Comput., 11(2): 1679–1696. http://dx.doi.org/10.1016/j.asoc.2010.04.024

[22] Martin, W., Flandrin, P., 1985. Wigner-Ville spectral analysis of nonstationary processes. IEEE Trans. Acoust. Speech Signal Process., 33(6):1461–1470. http://dx.doi.org/10.1109/TASSP.1985.1164760

[23] Martínez-Martínez, V., Gomez-Gil, F.J., Gomez-Gil, J., , 2015. An artificial neural network based expert system fitted with genetic algorithms for detecting the status of several rotary components in agro-industrial machines using a single vibration signal. Expert Syst. Appl., 42(17-18):6433–6441. http://dx.doi.org/10.1016/j.eswa.2015.04.018

[24] Moosavian, A., Ahmadi, H., Tabatabaeefar, A., , 2013. Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing. Shock Vibr., 20(2):263–272. http://dx.doi.org/10.3233/SAV-2012-00742

[25] Rilling, G., Flandrin, P., Gonçalvès, P., 2003. On empirical mode decomposition and its algorithms. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, p.1–5.

[26] Shibata, R., 1976. Selection of the order of an autoregressive model by Akaike’s information criterion. Biometrics, 63(1):117–126. http://dx.doi.org/10.1093/biomet/63.1.117

[27] Storn, R., Price, K., 1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim., 11(4):341–359. http://dx.doi.org/10.1023/A:1008202821328

[28] Su, Z., Tang, B., Liu, Z., , 2015. Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing, 157:208–222. http://dx.doi.org/10.1016/j.neucom.2015.01.016

[29] Suykens, J.A., Vandewalle, J., 1999. Multiclass least squares support vector machines. Int. Joint Conf. on Neural Networks, p.900–903. http://dx.doi.org/10.1109/IJCNN.1999.831072

[30] Tay, F.E.H., Shen, L., 2003. Fault diagnosis based on rough set theory. Eng. Appl. Artif. Intell., 16(1):39–43. http://dx.doi.org/10.1016/S0952-1976(03)00022-8

[31] Torres, M.E., Colominas, M., Schlotthauer, G., , 2011. A complete ensemble empirical mode decomposition with adaptive noise. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.4144–4147. http://dx.doi.org/10.1109/ICASSP.2011.5947265

[32] Vapnik, V.N., 1999. An overview of statistical learning theory. IEEE Trans. Neur. Netw., 10(5):988–999. http://dx.doi.org/10.1109/72.788640

[33] Vong, C.M., Wong, P.K., 2011. Engine ignition signal diagno-sis with wavelet packet transform and multi-class least squares support vector machines. Expert Syst. Appl., 38(7):8563–8570. http://dx.doi.org/10.1016/j.eswa.2011.01.058

[34] Wang, C., Zhang, Y., Zhong, Z., 2008. Fault diagnosis for diesel valve trains based on time-frequency images. Mech. Syst. Signal Process., 22(8):1981–1993. http://dx.doi.org/10.1016/j.ymssp.2008.01.016

[35] Wang, X., Liu, C., Bi, F., , 2013. Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD- fractal dimension.Mech. Syst. Signal Process., 41(1): 581–597. http://dx.doi.org/10.1016/j.ymssp.2013.07.009

[36] Wu, Z., Huang, N.E., 2009. Ensemble empirical mode de-composition: a noise-assisted data analysis method. Adv. Adapt. Data Anal., 1(1):1–41. http://dx.doi.org/10.1142/S1793536909000047

[37] Xie, Z., Shepard, W.S. Jr., Woodbury, K.A., 2009. Design optimization for vibration reduction of viscoelastic damped structures using genetic algorithms. Shock Vibr., 16(5):455–466. http://dx.doi.org/10.3233/SAV-2009-0480

[38] Xu, H., Chen, G., 2013. An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO. Mech. Syst. Signal Process., 35(1-2): 167–175. http://dx.doi.org/10.1016/j.ymssp.2012.09.005

[39] Xue, X., Zhou, J., Xu, Y., , 2015. An adaptively fast en-semble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mech. Syst. Signal Process., 62-63:444–459. http://dx.doi.org/10.1016/j.ymssp.2015.03.002

[40] Yang, K., Ouyang, G., Li, A., , 2015. Diesel engine misfire fault diagnosis based on instantaneous speed. Int. Conf. on Mechatronics, Electronic, Industrial and Control En-gineering, p.1497–1501. http://dx.doi.org/10.2991/meic-15.2015.343

[41] Zhang, X., Liang, Y., Zhou, J., , 2015. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement, 69:164–179. http://dx.doi.org/10.1016/j.measurement.2015.03.017

[42] Zhao, X., Ye, B., 2011. Selection of effective singular values using difference spectrum and its application to fault di-agnosis of headstock. Mech. Syst. Signal Process., 25(5): 1617–1631. http://dx.doi.org/10.1016/j.ymssp.2011.01.003

[43] Zheng, J.Y., Yang, Z.X., Wu, G.G., , 2015. FTA-SVM- based fault recognition for vehicle engine. IEEE 12th Int. Conf. on Networking, Sensing and Control, p.180–184. http://dx.doi.org/10.1109/ICNSC.2015.7116031

RIGHTS & PERMISSIONS

2017 Zhejiang University and Springer-Verlag Berlin Heidelberg