Qingmin Hou

Dr. Qingmin Hou

School of Smart City, Chengdu Vocational & Technical College of Industry, Chengdu, Sichuan, China
orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 November 2014 in Journal of Loss Prevention in the Process Industries
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Journal Article
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Qingmin HouQingmin Hou
Qingmin Hou
WJWenling Jiao
Wenling Jiao
LRLiang Ren
Liang Ren
HCHuizhe Cao
Huizhe Cao
GSGangbing Song
Gangbing Song

1 January 2013 in Mathematical Problems in Engineering
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Journal Article
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Qingmin HouQingmin Hou
Qingmin Hou
LRLiang Ren
Liang Ren
WJWenling Jiao
Wenling Jiao
PZPinghua Zou
Pinghua Zou
GSGangbing Song
Gangbing Song
Methods that more quickly locate leakages in natural gas pipelines are urgently required. In this paper, an improved negative pressure wave method based on FBG based strain sensors and wavelet analysis is proposed. This method takes into account the variation in the negative pressure wave propagation velocity and the gas velocity variation, uses the traditional leak location formula, and employs Compound Simpson and Dichotomy Searching for solving this formula. In addition, a FBG based strain sensor instead of a traditional pressure sensor was developed for detecting the negative pressure wave signal produced by leakage. Unlike traditional sensors, FBG sensors can be installed anywhere along the pipeline, thus leading to high positioning accuracy through more frequent installment of the sensors. Finally, a wavelet transform method was employed to locate the pressure drop points within the FBG signals. Experiment results show good positioning accuracy for natural gas pipeline leakage, using this new method.

1 November 2019 in Journal of Loss Prevention in the Process Industries
Verified
Journal Article
Closed access
Ziguang JiaZiguang Jia
Ziguang Jia
SHSiu-Chun Ho
Siu-Chun Ho
YLYang Li
Yang Li
BKBo Kong
Bo Kong
Qingmin HouQingmin Hou
Qingmin Hou

5 December 2020 in Mathematical Problems in Engineering
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Journal Article
Open access
Qingmin HouQingmin Hou
Qingmin Hou
DYDaheng Yang
Daheng Yang
XLXiaoyan Li
Xiaoyan Li
GXGuanghua Xiao
Guanghua Xiao
SHSiu Chun Michael Ho
Siu Chun Michael Ho
The leakage rate is an essential parameter for the risk assessment and failure analysis of natural gas pipelines. The leakage rate of a natural gas pipeline should be calculated quickly and accurately to minimize consequences. First, in this study, models to estimate the leakage rate of natural gas pipelines are reclassified, and the theoretical range of application for each model is also analysed. Second, the impact of the leakage on the flow rate upstream of the leak point is considered, and the method of successive approximation is used to realize this feedback effect of flow rate change. Then, a modified hole-pipe model is developed to calculate the natural gas leakage rate in this paper. Compared with the leakage rate calculated by the hole-pipe model, the leakage rate calculated by the modified hole-pipe model is smaller and closer to the actual leakage rate due to the consideration of the feedback effect of the flow rate change. Finally, the leakage rate curves of the hole-pipe model and the modified hole-pipe model under different d/D conditions are obtained through simulation. The simulation results show that the modified hole-pipe model is able to calculate the leakage rate of any leak aperture, such as the hole-pipe model, and also at a higher accuracy level than the hole-pipe model.

5 August 2019 in Applied Sciences
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Journal Article
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Qingmin HouQingmin Hou
Qingmin Hou
Weihang ZhuWeihang Zhu
Weihang Zhu
Small leaks in natural gas pipelines are hard to detect, and there are few studies on this problem in the literature. In this paper, a method based on the extended Kalman filter (EKF) is proposed to detect and locate small leaks in natural gas pipelines. First, the method of a characteristic line is used to establish a discrete model of transient pipeline flow. At the same time, according to the basic idea of EKF, a leakage rate is distributed to each segment of the discrete model to obtain a model with virtual multi-point leakage. As such, the virtual leakage rate becomes a component of the state variables in the model. Secondly, system noise and measurement noise are considered, and the optimal hydraulic factors such as leakage rate are estimated using EKF. Finally, by using the idea of an equivalent pipeline, the actual leakage rate is calculated and the location of leakage on the pipeline is assessed. Simulation and experimental results show that this method can consistently predict the leakage rate and location and is sensitive to small leakages in a natural gas pipeline.

15 March 2021 in Mathematical Problems in Engineering
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Journal Article
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Qingmin HouQingmin Hou
Qingmin Hou
Natural gas pipeline leaks can lead to serious and dangerous accidents that can cause great losses of life and property. Therefore, detecting natural gas pipeline leaks has always been an important subject. The negative pressure wave (NPW) method is currently the most widely used leakage detection method. Generally, this method uses pressure sensors to detect NPW signals to assess the leak and determine the location of the leakage point. However, the installation of a pressure sensor requires penetrating the pipeline structure, so the sensor intervals are often distant, leading to large signal attenuations and the ineffective detection of small leaks. An NPW method based on fiber Bragg grating (FBG) strain sensors is proposed in this paper which detects NPWs by monitoring the annular strain of the pipeline. Moreover, due to the advantages of nondestructive installation FBG strain sensors can be arranged closer along the distance of the pipeline, the attenuation of the NPW is small and the detection of leaks is improved. This method is tested through experiments and compared with a pressure sensor-based method; the experimental results verify that the proposed method is more effective in detecting natural gas pipeline leaks.