Study of Tank Acoustic Emission Testing Signals Analysis Method Based on Wavelet Neural Network

摘要：

In this paper, wavelet neural network is used to identify the acoustic emission signals from different types of tanks. Using wavelet transform and threshold denoising to denoise the detection signals, after wavelet packet decomposition, each nodes energy distribution and the feature vectors of extracted corrosion signals of the tank floor are selected as the input. At last, the compact-type wavelet neural network is chosen to recognize different AE signals. The result of magnetic flux leakage test proves that this method can improve acoustic emission signal analysis precision and achieve the accurate corrosion evaluation based on AE technology.

作者: Li Wei Dai Guang Zhang Ying Long Feifei Yang Zhijun

作者单位: Northeast Petroleum University, Daqing, 163318 China

会议类型: 国际会议

会议名称: World Conference on Acoustic Emission(2011年声发射国际会议)

会议地点: 北京

会议语种:英文

页码: 350-355

在线出版日期: 2011-08-24（万方平台首次上网日期，不代表论文的发表时间）