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Sensors 2013, 13(9), 12375-12391; doi:10.3390/s130912375

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

State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China
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Received: 16 July 2013 / Revised: 28 August 2013 / Accepted: 9 September 2013 / Published: 13 September 2013
(This article belongs to the Section Physical Sensors)
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Abstract

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.
Keywords: phased array ultrasonic transducer; artificial neural networks; low-pressure turbine disc; crack orientation; crack depth; RBF phased array ultrasonic transducer; artificial neural networks; low-pressure turbine disc; crack orientation; crack depth; RBF
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Yang, X.; Chen, S.; Jin, S.; Chang, W. Crack Orientation and Depth Estimation in a Low-Pressure Turbine Disc Using a Phased Array Ultrasonic Transducer and an Artificial Neural Network. Sensors 2013, 13, 12375-12391.

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