Wavelet package and neural network are used to recognize the characteristics of pipeline leakage acoustic signals. Acoustic signals produced by pressure variation of pipelines can be detected by the acoustic sensors installed on the pipelines. The detecting accuracy can be increased with recognizing the acoustic signals correctly. The method to detect acoustic signals by combining the wavelet package and neural network is introduced in this paper. The signal is decomposed with wavelet package firstly, then the decomposed coefficients in each frequency band are obtained through reconstruction. As a result, the parameters of the new sequences reconstructed on every decomposed node are acquired, and then these parameters are input to BP neural network to recognize the fault reason intelligently. At the end of the paper, field experiment data and their analyzed results are studied. The experimental results are provided to show that the proposed method can increase the accuracy efficiently.
- Pipeline Division
Study on Method of Recognizing Characteristics of Pipeline Leakage Acoustic Signals
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Wang, L, Tan, D, Cai, Y, Fu, S, Li, J, & Jin, S. "Study on Method of Recognizing Characteristics of Pipeline Leakage Acoustic Signals." Proceedings of the 2006 International Pipeline Conference. Volume 3: Materials and Joining; Pipeline Automation and Measurement; Risk and Reliability, Parts A and B. Calgary, Alberta, Canada. September 25–29, 2006. pp. 751-755. ASME. https://doi.org/10.1115/IPC2006-10262
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