Crack Orientation and Depth Estimation in a Low-Pressure Turbine Disc Using a Phased Array Ultrasonic Transducer and an Artificial Neural Network

Sensors

Xiaoxia Yang¹  Shili Chen¹  Shijiu Jin² 

[1] State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China;

关键词: phased array ultrasonic transducer;    artificial neural networks;    low-pressure turbine disc;    crack orientation;    crack depth;    RBF;   

DOI  :  10.3390/s130912375

来源: mdpi

【 摘 要 】

Stress corrosion cracks (SCC) in low-pressure steam turbine discs are serious hidden dangers to production safety in the power plants, and knowing the orientation and depth of the initial cracks is essential for the evaluation of the crack growth rate, propagation direction and working life of the turbine disc. In this paper, a method based on phased array ultrasonic transducer and artificial neural network (ANN), is proposed to estimate both the depth and orientation of initial cracks in the turbine discs. Echo signals from cracks with different depths and orientations were collected by a phased array ultrasonic transducer, and the feature vectors were extracted by wavelet packet, fractal technology and peak amplitude methods. The radial basis function (RBF) neural network was investigated and used in this application. The final results demonstrated that the method presented was efficient in crack estimation tasks.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.