Research on acoustic signal recognition method for pipeline leakage with BP neural network
Jiao Jingpin;Li Yongqiang;Wu Bin;He Cunfu;College of Mechanical Engineering and Application Electronics Technology,Beijing University of Technology;
In view of the urban water supply pipeline leak detection,the method of leak acoustic signal recognition is studied. The features of time- domain,frequency- domain and waveform of the leakage signals are analyzed,20 features which can be used to characterize the leakage signal are extracted. Based on the features,the BP neural network identification system for leakage acoustic signal is constructed.The influences of the neural network structure( the number of hidden nodes,transfer function,learning rate) and the number and type of the input parameters on the leakage signal recognition performance are studied,the best structure and input parameters of the neural network are optimized. Based on the above research,the optimized neural network was used to cross- train and identify the leak signal of the laboratory and water supply pipelines. The overall recognition rate reaches 92. 5%. The results show that the neural network system based on the leakage features has high reliability and universality,which can be well recognition the leakage signals under different scenarios. The research work has done a useful exploration to solve the leakage signal identification under different working conditions.