State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2013 Sep 13;13(9):12375-91. doi: 10.3390/s130912375.
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.
汽轮机低压叶片的应力腐蚀裂纹（SCC）是发电厂安全生产的严重隐患，了解初始裂纹的方向和深度对于评估叶片的裂纹扩展速率、扩展方向和工作寿命至关重要。本文提出了一种基于相控阵超声换能器和人工神经网络（ANN）的方法，用于估计涡轮叶片初始裂纹的深度和方向。通过相控阵超声换能器采集具有不同深度和方向的裂纹的回波信号，并通过小波包、分形技术和峰值幅度方法提取特征向量。研究了径向基函数（RBF）神经网络在该应用中的应用。最终结果表明，所提出的方法在裂纹估计任务中是有效的。
