Similar articles

Abstract: Targeting the mode-mixing problem of intrinsic time-scale decomposition (ITD) and the parameter optimization problem of least-square support vector machine (LSSVM), we propose a novel approach based on complete ensemble intrinsic time-scale decomposition (CEITD) and LSSVM optimized by the hybrid differential evolution and particle swarm optimization (HDEPSO) algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components (PRCs) and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.

The paper presents a data driven approach to the analysis of nonstationary engine vibration signals for fault classification. The approach is developed by combining a number of computing techniques including intrinsic time-scale decomposition (ITD) and the parameters optimization problem of least square support vector machine (LSSVM), differential evolution and particle swarm, optimization (HDEPSO) algorithms, which are used for processing the signals and selecting features, and least square support vector machine(LLSVM) for classification. Especially, the development of the proposed ensemble intrinsic time-scale decomposition looks intersting.

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

[1]Ardia, D., Boudt, K., Carl, P., et al., 2011. Differential evolution with DEoptim: an application to non-convex portfolio optimization. R J., 3(1):27-34.

[2]Chen, B.J., He, Z.J., Chen, X.F., et al., 2011. A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis. Meas. Sci. Technol., 22(5):055704.

[3]Chen, M., Zheng, A.X., Jordan, M.I., et al., 2004. Failure diagnosis using decision trees. Int. Conf. on Autonomic Computing, p.36-43.

[4]Cheng, J.S., Yu, D.J., Yang, Y., 2006. A fault diagnosis approach for roller bearings based on EMD method and AR model. Mech. Syst. Signal Process., 20(2):350-362.

[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).

[6]Cheng, M.Y., Hoang, N.D., Wu, Y.W., 2013. Hybrid intelligence approach based on LS-SVM and differential evolution for construction cost index estimation: a Taiwan case study. Autom. Constr., 35:306-313.

[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.

[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.

[9]Hong, H., Wang, X.L., Tao, Z.Y., et al., 2011. Centroid-based sifting for empirical mode decomposition. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 12(2):88-95.

[10]Huang, J., Hu, X., Geng, X., 2011. An intelligent fault diagnosis 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.

[11]Huang, N.E., Shen, Z., Long, S.R., et al., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. A, 454(1971):903-995.

[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.

[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.

[15]Lei, Y.G., He, Z.J., Zi, Y.Y., et al., 2007. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mech. Syst. Signal Process., 21(5):2280-2294.

[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.

[17]Li, J., Li, S., Chen, X., et al., 2015. The hybrid KICA-GDA-LSSVM method research on rolling bearing fault feature extraction and classification. Shock Vibr., 2015:1-9.

[18]Li, Y., Tse, P.W., Yang, X., et al., 2010. EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine. Mech. Syst. Signal Process., 24(1):193-210.

[19]Li, Z., Yan, X., Yuan, C., et al., 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.

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

[21]Mallipeddi, R., Suganthan, P.N., Pan, Q.K., et al., 2011. Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput., 11(2):1679-1696.

[22]Martin, W., Flandrin, P., 1985. Wigner-Ville spectral analysis of nonstationary processes. IEEE Trans. Acoust. Speech Signal Process., 33(6):1461-1470.

[23]Martínez-Martínez, V., Gomez-Gil, F.J., Gomez-Gil, J., et al., 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.

[24]Moosavian, A., Ahmadi, H., Tabatabaeefar, A., et al., 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.

[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.

[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.

[28]Su, Z., Tang, B., Liu, Z., et al., 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.

[29]Suykens, J.A., Vandewalle, J., 1999. Multiclass least squares support vector machines. Int. Joint Conf. on Neural Networks, p.900-903.

[30]Tay, F.E.H., Shen, L., 2003. Fault diagnosis based on rough set theory. Eng. Appl. Artif. Intell., 16(1):39-43.

[31]Torres, M.E., Colominas, M., Schlotthauer, G., et al., 2011. A complete ensemble empirical mode decomposition with adaptive noise. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.4144-4147.

[32]Vapnik, V.N., 1999. An overview of statistical learning theory. IEEE Trans. Neur. Netw., 10(5):988-999.

[33]Vong, C.M., Wong, P.K., 2011. Engine ignition signal diagnosis with wavelet packet transform and multi-class least squares support vector machines. Expert Syst. Appl., 38(7):8563-8570.

[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.

[35]Wang, X., Liu, C., Bi, F., et al., 2013. Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension. Mech. Syst. Signal Process., 41(1):581-597.

[36]Wu, Z., Huang, N.E., 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal., 1(1):1-41.

[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.

[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.

[39]Xue, X., Zhou, J., Xu, Y., et al., 2015. An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mech. Syst. Signal Process., 62-63:444-459.

[40]Yang, K., Ouyang, G., Li, A., et al., 2015. Diesel engine misfire fault diagnosis based on instantaneous speed. Int. Conf. on Mechatronics, Electronic, Industrial and Control Engineering, p.1497-1501.

[41]Zhang, X., Liang, Y., Zhou, J., et al., 2015. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement, 69:164-179.

[42]Zhao, X., Ye, B., 2011. Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock. Mech. Syst. Signal Process., 25(5):1617-1631.

[43]Zheng, J.Y., Yang, Z.X., Wu, G.G., et al., 2015. FTA-SVM-based fault recognition for vehicle engine. IEEE 12th Int. Conf. on Networking, Sensing and Control, p.180-184.