Condition prediction of hydroturbine generating units using least squares support vector regression with genetic algorithms

The least squares support vector regression (LSSVR), a least squares version of standard support vector regression, is applied in condition forecast of hydroturbine generating units (HGUs) by its vibration signal time series in this paper. An effective LSSVR model can only be built under suitable parameters. A novel approach, named as GA-LSSVR, is proposed in this paper, which searches for the optimal parameters of LSSVR model using real-value genetic algorithms and adopts the optimal parameters to construct the LSSVR model. The peak-peak value (ppv) time series data of the stator vibration signals in HGUs were used as the data set. The experimental results are shown that the GA-LSSVR model outperforms the existing BP neural network approaches and the simple LSSVR based on the mean absolute percent error criterion.

[1]  Chi-Man Vong,et al.  Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference , 2006, Eng. Appl. Artif. Intell..

[2]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[3]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[4]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[5]  Jingqing Liu,et al.  A method determining parameters of SVR model based on Probability and Statistics , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[6]  Fa-chao Li,et al.  Parameter Optimization for SVM using Sequential Number Theoretic for Optimization , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[7]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

[8]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[9]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[10]  Youshen Xia,et al.  A prediction fusion method for reconstructing spatial temporal dynamics using support vector machines , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[11]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[12]  J. Suykens.  Nonlinear modelling and support vector machines , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[13]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[14]  S. Sathiya Keerthi,et al.  Evaluation of simple performance measures for tuning SVM hyperparameters , 2003, Neurocomputing.

[15]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .