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There has been a sustained interest over the past couple of decades in developing sophisticated leak detection techniques (LDTs) that are economical and reliable. Majority of current commercial LDTs are acoustics based and they are not equally suitable to all pipe materials and sizes. There is also limited knowledge on the comparative merits of such acoustics-based leak detection techniques (ALDTs). The purpose of this paper is to review six commercial ALDTs based on four decisive criteria and subsequently develop guidance for the optimal selection of an ALDT.

Numerous publications and field demonstration reports are reviewed for evaluating the performance of various ALDTs in this study to inform their optimal selection using an integrated multi-criteria decision analysis (MCDA) framework. The findings are validated using interviews of water utility experts.

The study approach and the findings will have a broad impact on the water utility industry by identifying a suite of suitable ALDTs for a range of typical application scenarios. The evaluated ALDTs include listening devices, noise loggers, leak-noise correlators, free-swimming acoustic, tethered acoustic, and acoustic emissions. The evaluation criteria include cost, reliability, access requirements, and the ability to quantify leakage severity. The guidance presented in this paper will support efficient decision making in water utility management to minimize pipeline leakage.

This study attempts to address the problem of severe dearth of performance data for pipeline inspection techniques. Performance data reported in the published literature on various ALDTs are appropriately aggregated and compared using a MCDA, while the uncertainty in performance data is addressed using the Monte Carlo simulation approach.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.

The purpose of this study is to develop a robot for non-destructive testing of the pipelines to improve its reliability and reduce the loss of products due to cracks, corrosions, etc.

In this study, an inline inspection robot was developed for crack and corrosion detection in the pipeline. The developed robot consists of ultrasonic sensors to avoid obstacles, a visual aid with high resolution to view real time images and colour sensors for corrosion detection. The Autodesk inventor software was used for the drafting and solid modelling of the robot. A dummy pipe of 500 mm diameter and 2,000 mm length with induced cracks and corrosion was fabricated to test the robot. The colour sensors placed at each side of the robot were used to detect corrosion in the dummy pipe whilst the image processing was done to analyse the crack, as well as the type and depth of corrosion present in the dummy pipe.

The results obtained show the ability of the developed robot to detect cracks and determine the crack growth in the pipeline in addition to its ability to determine corrosion.

Hence, the study provides a diagnostic tool for detecting pipeline defects and analysing the extent of defects to determine the fatigue rate and the useful life of the pipeline.

The novelties of this study is based on the fact that it was designed to avoid obstacles and check for cracks, leakage and corrosion in pipelines autonomously. It has visual aid that makes it possible to see the interior of the pipe. This makes it easier to identify the defect and the location of the defects before a catastrophic failure. The device is also equipped with sensors, which can detect defects and send the signal to a control system, as well as a Bluetooth device so the operator can have real time information about the state and integrity of the pipelines. The system is also integrated with a Bluetooth device, which permits its compatibility with Android and other mobile applications. Thus, the enabled user can send a command to query the state of the pipeline at any location with the feedback received in the form of short message service. Hence, this study offers contribution in the development of an independent (self-governing) system with the capability to autonomously detect defects in pipe walls and effectively communicate feedback to the authorised users. The prototype model for the evaluation of pipeline integrity will bring about a more proactive way to detect pipeline defects so that effort can be geared towards its restoration before it becomes a major problem, which will subsequently affect productivity and incur losses.

The purpose of this paper is to develop a novel autonomous in‐pipe robot to perform the preventive point reparation for long‐distance offshore oil pipelines.

The autonomous in‐pipe robot performs online ultrasonic inspection for pipe wall thickness, and the original inspection data are stored in large capacity hard disk. Through the offline data analysis by the data analysts and the software tool, the pipeline health status is known. If server defects lie there, the in‐pipe robot is introduced into the pipeline once more to indicate the defect's location to the maintenance ship.

The laboratory tests and the field tests prove the feasibility and validity of the developed autonomous in‐pipe robot. Furthermore, the application of intelligent control techniques ensures the mission completion by the autonomous in‐pipe robot, which worked in the awful pipeline environment.

The developed autonomous in‐pipe robot helps eliminate lost production costs and pipeline downtime caused by leakages and guarantees the safe run of offshore oil pipelines.

For the application of the autonomous in‐pipe robot, there are no special requirements for maintained pipelines themselves, so it is applicable to the point reparation for most long‐distance welded offshore pipelines.

The purpose of this paper is to present a new technique for monitoring gas leakage in underground pipelines to prevent dangerous explosions.

A novel system for monitoring methane concentration in underground spaces was developed by integrating the multi-channeled air sampling method with an infrared gas sensor. A pipe installation methodology (without excavation) was established and verified accordingly.

The proposed approach was proven successful in reducing the quantity of sensors needed for real-time monitoring of underground pipeline leakage by about 80 per cent. Furthermore, this system lowers total operational cost by as much as 60 per cent.

The results presented here represent a possible solution to reducing the public safety risks associated with explosions and fires caused by pipeline leakage in underground spaces. Its total cost is low and its monitoring efficiency is high.

This paper aims to examine the reliability of a cooling seawater pumping station which pumps seawater to refineries and petrochemical plants in Kuwait mainly for cooling purposes. Owing to the harsh operating climate and the lack of other alternative sources of water, high reliability of the pumping system is crucially important.

The reliability of a system can be considered from different views. The one taken in this study is to measure the reliability of the cooling seawater arriving to the consumer at the required pressure and flow rate while observing the operational constraints on the system. A reliability model has been developed and applied to the actual data from the pumping station.

The preliminary results indicate that the model could be a useful tool to assess the current condition of the system and to find ways of improving system reliability and availability.

The paper provides information of value to all concerned with relibility management.

Since the introduction of the fibre optic distributed temperature system by York in 1986, the system has been successfully applied in a number of areas. The monitoring of hot‐spots in large power transformers and electricity cables to the analysis of the curing process in large concrete structures such as dams are some examples of where the technology is being used and is providing temperature data. Other more imaginative but nevertheless potentially feasible applications range from detecting black ice on roads to the monitoring of volcanic eruptions for the purpose of disaster prevention.

Acoustic signals of the underwater targets are susceptible to noise, reverberation, submarine topography and biology, therefore it is difficult to precisely locate underwater targets. This paper proposes a new underwater Hanbury Brown-Twiss (HBT) interference passive localization method. This study aims to achieve precise location of the underwater acoustic targets.

The principle of HBT interference with ultrasensitive detection characteristics in optical measurements was introduced in the field of hydroacoustics. The coherence of the underwater target signal was analyzed using the HBT interference measurement principle, and the corresponding relationship between the signal coherence and target position was obtained. Consequently, an HBT interference localization model was established, and its validity was verified through simulations and experiments.

The effects of different array structures on the localization performance were obtained by simulation analysis, and the simulations confirmed that the HBT method exhibited a higher positioning accuracy than conventional beamforming. In addition, the experimental analysis demonstrated the excellent positioning performance of the HBT method, which verified the feasibility of the proposed method.

This study provides a new method for the passive localization of underwater targets, which may be widely used in the field of oceanic explorations.

The purpose of this paper is to provide a review of environmental sensing technologies and applications and to consider briefly the research effort.

This paper describes environmental sensing strategies and technologies for air, water quality and other monitoring applications. It concludes with a brief overview of research.

As a consequence of legislation, a technologically diverse family of sensors are used in a wide range of portable and fixed instruments to determine numerous polluting compounds entering and present in the environment. These play a critical role in protecting the environment, maintaining public health and monitoring global phenomena such as ozone depletion and artificial global warming. There is a widespread and technologically diverse research effort.

This paper provides a detailed insight into the sensors and instruments used in environmental monitoring.

Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology in all fields and the high failure rate of software development projects, it is essential to predict the risk level of each project effectively before starting. Therefore, the main purpose of this paper is proposing an expert system to infer about the risk of new banking software development project.

In this research, the risk of software developing projects is considered from four dimensions including risk of cost deviation, time deviation, quality deviation and scope deviation, which is examined by rough set theory (RST). The most important variables affecting the cost, time, quality and scope of projects are identified as condition attributes and four initial decision systems are constructed. Grey system theory is used to cluster the condition attributes and after data discretizing, eight rule models for each dimension of risk as a decision attribute are extracted using RST. The most validated model for each decision attribute is selected as an inference engine of the expert system, and finally a simple user interface is designed in order to predict the risk level of any new project by inserting the data of project attributes

In this paper, a high accuracy expert system is designed based on the combination of the grey clustering method and rough set modeling to predict the risks of each project before starting. Cross-validation of different rule models shows that the best model for determining cost deviation is Manual/Jonson/ORR model, and the most validated models for predicting the risk of time, quality and scope of projects are Entropy/Genetic/ORR, Manual/Genetic/FOR and Entropy/Genetic/ORR models; all of which are more than 90% accurate

It is essential to gather data of previous cases to design a validated expert system. Since data documentation in the field of software development projects is not complete enough, grey set theory (GST) and RST are combined to improve the validity of the rule model. The proposed expert system can be used for risk assessment of new banking software projects

The risk assessment of software developing projects based on RST is a new approach in the field of risk management. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.