SIGNAL (programming language)

Engineering

Frequency domain

Leak

Acoustic emission

Computer science

Acoustics

Mechanical engineering

Environmental engineering

Hydrophone

Physics

Pipeline (software)

Artificial intelligence

Body orifice

Time domain

Pipeline transport

Programming language

Artificial neural network

Computer vision

Measurement

Water pipeline leakage is a common and significant global problem. In-pipe inspection based on hydrophone is one of the most direct, accurate, and reliable solutions for leak detection and recognition. In this study, a scheme of in-pipe detector was designed to pick up and identify acoustic signal due to leak. To investigate the characteristic of acoustic signal, an experimental platform was built to simulate the leaks and obtain acoustic signals under different leak conditions in an industrial scale water pipeline. Because a decreased pressure as leak has an unstable fluctuation in time domain, the frequency composition of the signal was analyzed in frequency domain, and then the change of frequency amplitude can be referenced to recognize the leaks. Moreover, the effects of leak size, pipeline pressure, and water flow rate on the characteristic of acoustic signal were investigated. The results show that the signal’s intensity under leak conditions are significantly higher than that of no leak case, and it will increase as the increased leak size; the signal intensity under no leak case will increase with the growth of pipeline pressure; the flow velocity has little effect on the signal intensity. To increase the recognition accuracy, an artificial neural network model was developed for the leak prediction, and 18 cases through additional tests were selected to validate the accuracy of model. Comparing experimental and prediction results, maximum relative error is within 10.0%. It indicates that the prediction model has a reasonable accuracy for the leak recognition.

Experimental study on water pipeline leak using In-Pipe acoustic signal analysis and artificial neural network prediction