Damage localization in hydraulic turbine blades using kernel-independent component analysis and support vector machines

Abstract

A hydraulic turbine runner has a complex structure, and traditional source location methods do not have the higher accuracy to meet engineering requirements. The source location of crack acoustic emission (AE) signals in hydraulic turbine blades has been researched by combining it with kernel-independent component analysis (KICA) as feature extraction, with support vector machines (SVMs) as position recognition. This method is compared with those applied SVMs with feature extraction using kernel principal components analysis without feature extraction. The results show that the recognition rate in the crack region is 100 per cent by using both original AE parameters and feature parameters. Support vector regression by feature extraction using KICA can perform better than the other methods. As a result, it is a better method for source location of complex big size structures to combine KICA with SVM. It decreases the dimensionality of input signals and also improves the accuracy of location.

Keywords

support vector machines source location acoustic emission hydraulic turbine kernel independent component analysis

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References

Nivesrangsan

Steel

J. A.

Reuben

R. L.

, Source location of acoustic emission in diesel engines Mech. Syst. Signal Process 21 2 (2007): 1103–1114.

Zuluaga-Giraldo

Mba

Smart

, Acoustic emission during run-up and run-down of a power generation turbine Tribol. Int. 37 5 (2004): 415–422.

Widodo

Yang

B.-S.

, Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors Expert Syst. Appl. 33 1 (2007): 241–250.

Wang

Ding

Sun

Y. A.

Sun

, Estimation of source infrared spectra profiles of acetylspiramycin active components from troches using kernel independent component analysis Spectrochin. Acta Pt. A—Mol. Biomol. Spectrosc. 70 3 (2008): 571–576.

Axinte

D. A.

Natarajan

D. R.

Gindy

N. N. Z.

, An approach to use an array of three acoustic emission sensors to locate uneven events in machining — part 1: Method and validation Int. J. Mach. Tool. Manuf. 45 14 (2005): 1605–1613.

Jack

L. B.

Nandi

A. K.

, Support vector machines for detection and characterization of rolling element bearing faults Proc. Instn Mech. Engrs, Part C: J. Mechanical Engineering Science 215 9 (2001): 1065–1074.

Hyvarinen

, Fast and robust fixed-point algorithms for independent component analysis IEEE Trans. Neural Netw. 10 3 (1999): 626–634.

Nivesrangsan

Steel

J. A.

Reuben

R. L.

, AE mapping of engines for spatially located time series Mech. Syst. Signal Process. 19 5 (2005): 1034–1054.

ASTM. ASTM E976–99: Standard guild for determining the reproducibility of acoustic emission sensor response In Annual Book of ASTM Standard, 1999 vol. 3.03, pp. 395–403.

10.

S. X.

, Cracks in turbine runner and its treatment (in Chinese) Mech. Electr. Tech. Hydropower Stn. 3 (1992): 1–7.