Stochastic modeling of leak detection and localization using ultrasonic sensor array

Author(s):  
Hossein Roufarshbaf ◽  
Joel Castro ◽  
Ali Abedi
2021 ◽  
pp. 1-12
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim

Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective technique for leak detection and localization is presented in this study. The proposed scheme has four main steps. First, the learning autoregressive technique is selected to approximate the flow signal under normal conditions and extract the mathematical state-space formulation with uncertainty estimations using a combination of robust autoregressive and support vector regression techniques. In the next step, the intelligence-based learning observer is designed using a combination of the robust learning backstepping method and a fuzzy-based technique. The learning backstepping algorithm is the main part of the algorithm that determines the leak estimation. After estimating the signals, in the third step, their classification is performed by the support vector machine algorithm. Finally, to find the size and position of the leak, the multivariable backstepping algorithm is recommended. The effectiveness of the proposed learning control algorithm is analyzed using both experimental and simulation setups.


Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2985 ◽  
Author(s):  
Tao Wang ◽  
Xiaoran Wang ◽  
Mingyu Hong

Ultrasonic gas leak location technology is based on the detection of ultrasonic waves generated by the ejection of pressured gas from leak holes in sealed containers or pipes. To obtain more accurate leak location information and determine the locations of leak holes in three-dimensional space, this paper proposes an ultrasonic leak location approach based on multi-algorithm data fusion. With the help of a planar ultrasonic sensor array, the eigenvectors of two individual algorithms, i.e., the arrival distance difference, as determined from the time difference of arrival (TDOA) location algorithm, and the ratio of arrival distances from the energy decay (ED) location algorithm, are extracted and fused to calculate the three-dimensional coordinates of leak holes. The fusion is based on an extended Kalman filter, in which the results of the individual algorithms are seen as observation values. The final system state matrix is composed of distances between the measured leak hole and the sensors. Our experiments show that, under the condition in which the pressure in the measured container is 100 kPa, and the leak hole–sensor distance is 800 mm, the maximum error of the calculated results based on the data fusion location algorithm is less than 20 mm, and the combined accuracy is better than those of the individual location algorithms.


2018 ◽  
Vol 24 (2) ◽  
pp. 189-194
Author(s):  
Yuichi Morita ◽  
Sota Kono ◽  
Akira Yamawaki

2014 ◽  
Vol 33 (3) ◽  
pp. 458-470 ◽  
Author(s):  
A. Gajdacsi ◽  
A. J. C. Jarvis ◽  
P. Huthwaite ◽  
F. B. Cegla

2003 ◽  
Vol 12 (4) ◽  
pp. 506-512 ◽  
Author(s):  
Yaowu Mo ◽  
T. Tanaka ◽  
K. Inoue ◽  
K. Yamashita ◽  
Y. Suzuki

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