Sub‐optimum fast Bayesian techniques for joint leak detection and localisation. Issue 3 (1st September 2013)

• Record Type: Journal Article
• Authors: Roufarshbaf, Hossein; Castro, Joel; Schwaner, Fred; Abedi, Ali
• Is Part Of: IET wireless sensor systems. Volume 3:Issue 3(2013)
• Page Start: 239
• Page End: 246
• Publication Date: 2013-09-01
• Subjects: acoustic signal processing -- approximation theory -- Bayes methods -- particle filtering (numerical methods) -- search problems -- wireless sensor networks; suboptimum fast Bayesian techniques -- joint leak detection -- joint leak localisation -- tree search algorithm -- surface borne ultrasonic acoustic signals -- wireless sensor network -- environmental noise -- multipath fading -- ultrasonic signals -- false sensor observations -- observation data -- Bayesian inference model -- tree‐search structure -- tree structure -- Bayesian techniques -- particle filtering; Wireless sensor networks -- Periodicals; 681.2
• DOI: 10.1049/iet-wss.2012.0137
• Languages: English
• ISSNs: 2043-6386

Abstract

Abstract : A fast tree‐search algorithm for joint leak detection and localisation using surface‐borne ultrasonic acoustic signals is developed through a wireless sensor network. Owing to environmental noise and multipath fading of ultrasonic signals, false sensor observations are frequent in the observation data. The problem is modelled as a Bayesian inference model and the maximum a posteriori solution is approximated through a tree‐search structure. The algorithm initially divides the area into large cells and approximates the observation likelihood function over these large cells. In a tree structure, a large cell with high likelihood is divided into smaller cells and the tree is expanded until the required estimation precision is obtained. Simulation and experimental results reveal advantages of the proposed technique in terms of estimation error and convergence speed in comparison with other conventional Bayesian techniques such as particle filtering.