Abstract

In the face of increased spatial distribution and a limited budget, monitoring critical regions of pipeline network is looked upon as an important part of condition monitoring through wireless sensor networks. To achieve this aim, it is necessary to target critical deployed regions rather than the available deployed ones. Unfortunately, the existing approaches face grave challenges due to the vulnerability of identification to human biases and errors. Here, we have proposed a novel approach to determine the criticality of different deployed regions by ranking them based on risk. The probability of occurrence of the failure event in each deployed region is estimated by spatial statistics to measure the uncertainty of risk. The severity of risk consequence is measured for each deployed region based on the total cost caused by failure events. At the same time, hypothesis testing is used before the application of the proposed approach. By validating the availability of the proposed approach, it provides a strong credible basis and the falsifiability for the analytical conclusion. Finally, a case study is used to validate the feasibility of our approach to identify the critical regions. The results of the case study have implications for understanding the spatial heterogeneity of the occurrence of failure in a pipeline network. Meanwhile, the spatial distribution of risk uncertainty is a useful priori knowledge on how to guide the random deployment of wireless sensors, rather than adopting the simple assumption that each sensor has an equal likelihood of being deployed at any location.

Highlights

  • With the application of condition based maintenance (CBM) in critical infrastructures, wireless sensor networks have gained so much popularity and are being deployed for sewage flood monitoring in sewer pipeline network, leakage monitoring in gas pipeline network, and strength monitoring of megastructures [1,2,3,4,5]

  • Wireless sensor network deployment is a great challenge in pipeline networks

  • To provide a better conveyance of information in results, the result of each sensor field are arranged in Table 6 according to the index which is calculated by Equation (9)

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Summary

Introduction

With the application of condition based maintenance (CBM) in critical infrastructures, wireless sensor networks have gained so much popularity and are being deployed for sewage flood monitoring in sewer pipeline network, leakage monitoring in gas pipeline network, and strength monitoring of megastructures [1,2,3,4,5]. Wireless sensor networks provide a key basis to help in assessing the assets’ or equipment’ condition, which is useful to guide the allocation of maintenance resource in time and space. The interval initiates the maintenance actions such as the repair or replacement, which essentially allocates the resources in time dimension. The guidance tells that where the parts of the critical infrastructure are likely to need maintenance. Despite the declining price of sensors, the cost remains high for the application, which requires hundreds and thousands of sensor

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