Browsing by Author "Zhang, Jifeng"

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    A dead reckoning localization method for in-pipe detector of water supply pipeline: an application to leak localization
    (Elsevier, 2020-12-09) Wang, Wenming; Yang, Dashan; Zhang, Jifeng; Lao, Liyun; Yin, Yuanfang; Zhu, Xiaoxiao
    Urban water supply pipeline system integrity is important for the urban life. The aim of the study reported in this paper is to locate the water pipeline leaks by using an in-pipe detector. In this study, a mathematical model is extracted from an actual inspection system. By using the homogeneous transformation theory, transformation matrix which is from carrier to a reference coordinate system is deduced, and then the global transformation matrix is obtained to describe the detector’s posture. Through measuring the distance increment of each sample time step in carrier coordinate system, the cumulative distance result is calculated. After combining the data of the inertial measurement unit (IMU) and odometer, the leak can be located. To improve the accuracy of leak localization, the magnetic markers are implemented about one in each 1 km distance, which provide reference points to be used to compensate accumulative error during the localization process. Furthermore, a dead reckoning localization method combining data of a micro electro-mechanical IMU, three odometers, and magnetic markers is proposed. To verify above localization algorithm, a simulation case study is conducted with the artificial error generated by the white noise. The simulation results show that the dead reckoning algorithm can effectively provide leak locations with a reasonable uncertainty. Based on this, an experimental platform was built in this study. The experimental results show that the relative error of leak locating achieves a reasonably good performance
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    Experimental study on water pipeline leak using in-pipe acoustic signal analysis and artificial neural network prediction
    (Elsevier, 2021-08-30) Wang, Wenming; Sun, Haibo; Guo, Jianqiang; Lao, Liyun; Wu, Shide; Zhang, Jifeng
    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.