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Proceedings ArticleDOI

Health monitoring of offshore structures using wireless sensor network: experimental investigations

08 Apr 2016-Proceedings of SPIE (International Society for Optics and Photonics)-Vol. 9804, pp 980416
TL;DR: This paper presents a detailed methodology of deploying wireless sensor network in offshore structures for structural health monitoring (SHM) to determine the status of serviceability of large floating platforms under environmental loads using wireless sensors.
Abstract: This paper presents a detailed methodology of deploying wireless sensor network in offshore structures for structural health monitoring (SHM). Traditional SHM is carried out by visual inspections and wired systems, which are complicated and requires larger installation space to deploy while decommissioning is a tedious process. Wireless sensor networks can enhance the art of health monitoring with deployment of scalable and dense sensor network, which consumes lesser space and lower power consumption. Proposed methodology is mainly focused to determine the status of serviceability of large floating platforms under environmental loads using wireless sensors. Data acquired by the servers will analyze the data for their exceedance with respect to the threshold values. On failure, SHM architecture will trigger an alarm or an early warning in the form of alert messages to alert the engineer-in-charge on board; emergency response plans can then be subsequently activated, which shall minimize the risk involved apart from mitigating economic losses occurring from the accidents. In the present study, wired and wireless sensors are installed in the experimental model and the structural response, acquired is compared. The wireless system comprises of Raspberry pi board, which is programmed to transmit the acquired data to the server using Wi-Fi adapter. Data is then hosted in the webpage for further post-processing, as desired.
Citations
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Journal ArticleDOI

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01 Apr 2019
TL;DR: A time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties, to reduce the delivery latency caused by unreasonable task allocation and optimize the energy consumption, which makes the sensor-cloud sustainable.
Abstract: The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a bottleneck. Moreover, the limited power of sensor usually results in a short lifetime of WSNs. To solve these problems, we propose to use multiple mobile sinks (MSs) to help with data collection. We formulate a new problem which focuses on collecting data from WSNs to cloud within a limited time and this problem is proved to be NP-hard. To reduce the delivery latency caused by unreasonable task allocation, a time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties. In TASA, a non-overlapping and adjustable trajectory is projected for each MS. In addition, a minimum cost spanning tree (MST) based routing method is designed to save the transmission cost. We conduct extensive simulations to evaluate the performance of the proposed algorithm. The results show that the TASA can collect the data from WSNs to Cloud within the limited latency and optimize the energy consumption, which makes the sensor-cloud sustainable.

58 citations


Cites background from "Health monitoring of offshore struc..."

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Proceedings ArticleDOI

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01 Jun 2017
TL;DR: A novel hardware-software platform designed to monitor machinery in remote deployments and expedite collection of experimental data, which could also be used for structural monitoring, and can facilitate the execution of sensing experiments in rotating machinery and similar equipment is presented.
Abstract: Traditional wired vibration and acoustic sensors used for machine and structural monitoring are currently being replaced by low-cost MEMS-based wireless sensor networks (WSN). However, existing platforms are lacking in computing capabilities and integration, as well as the necessary software features to manage wireless sensing experiments. In this paper, we present a novel hardware-software platform designed to monitor machinery in remote deployments and expedite collection of experimental data, which could also be used for structural monitoring. The hardware module is composed of a single PCB with an IEEE 802.15.4-compatible microcontroller, waterproof Micro-USB connector, battery, battery charger/monitor, humidity/temperature sensor, IMU, and a MEMS microphone. The software developed allows for wireless experiment control and data collection through a gateway node connected to a laptop. Additionally, the user interface supports the placement of the nodes in a 3D view of the environment, as well as visualisation of the collected data. The platform was tested in the laboratory in two different motor setups by measuring vibration and sound in normal operation, showing that the system can facilitate the execution of sensing experiments in rotating machinery and similar equipment.

5 citations


Cites methods from "Health monitoring of offshore struc..."

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Journal ArticleDOI

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TL;DR: This study concludes that implementation of structural health monitoring (SHM) to offshore platforms ensures safe operability and structural integrity, and proposes a novel scheme of deploying wireless sensor network for this purpose.
Abstract: Offshore platforms are of high strategic importance, whose preventive maintenance is on top priority. Buoyant Leg Storage and Regasification Platforms (BLSRP) are special of its kind as they handle LNG storage and processing, which are highly hazardous. Implementation of structural health monitoring (SHM) to offshore platforms ensures safe operability and structural integrity. Prospective damages on the offshore platforms under rare events can be readily identified by deploying dense array of sensors. A novel scheme of deploying wireless sensor network is experimentally investigated on an offshore BLSRP, including postulated failure modes that arise from tether failure. Response of the scaled model under wave loads is acquired by both wired and wireless sensors to validate the proposed scheme. Proposed wireless sensor network is used to trigger alert monitoring to communicate the unwarranted response of the deck and buoyant legs under the postulated failure modes. SHM triggers the alert mechanisms on exceedance of the measured data with that of the preset threshold values; alert mechanisms used in the present study include email alert and message pop-up to the validated user accounts. Presented study is a prima facie of SHM application to offshore platforms, successfully demonstrated in lab scale.

2 citations

References
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TL;DR: Technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner and the historical overview and summarizing the SPR paradigm are provided.
Abstract: This introduction begins with a brief history of SHM technology development. Recent research has begun to recognise that a productive approach to the Structural Health Monitoring (SHM) problem is to regard it as one of statistical pattern recognition (SPR); a paradigm addressing the problem in such a way is described in detail herein as it forms the basis for the organisation of this book. In the process of providing the historical overview and summarising the SPR paradigm, the subsequent chapters in this book are cited in an effort to show how they fit into this overview of SHM. In the conclusions are stated a number of technical challenges that the authors believe must be addressed if SHM is to gain wider acceptance.

1,847 citations


"Health monitoring of offshore struc..." refers background in this paper

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Journal ArticleDOI

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TL;DR: The motivations for and recent history of SHM applications to various forms of civil infrastructure are described, the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural ‘health’ are discussed.
Abstract: Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued 'fitness for purpose' of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural 'health'.

688 citations


"Health monitoring of offshore struc..." refers background in this paper

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Journal ArticleDOI

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TL;DR: The results showed that the WSN provides spatially dense and accurate ambient vibration data for identifying vibration modes of a bridge and the scalability of the network and the data quality was demonstrated.
Abstract: An integrated hardware and software system for a scalable wireless sensor network WSN is designed and developed for structural health monitoring. An accelerometer sensor node is designed, developed, and calibrated to meet the requirements for structural vibration monitoring and modal identification. The nodes have four channels of accelerometers in two directions and a microcontroller for processing and wireless communication in a multihop network. Software components have been implemented within the TinyOS oper- ating system to provide a flexible software platform and scalable performance for structural health monitoring applications. These components include a protocol for reliable command dissemination through the network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. The prototype WSN was deployed on a long-span bridge with 64 nodes. The data acquired from the testbed were used to examine the scalability of the network and the data quality. Robust and scalable performance was demonstrated even with a large number of hops required for communication. The results showed that the WSN provides spatially dense and accurate ambient vibration data for identifying vibration modes of a bridge.

289 citations


"Health monitoring of offshore struc..." refers background in this paper

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Journal ArticleDOI

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19 Aug 2009-Sensors
TL;DR: The proposed water environmental monitoring system based on a wireless sensor network has successfully accomplished the online auto-monitoring of the water temperature and pH value environment of an artificial lake and promises broad applicability prospects.
Abstract: A water environmental monitoring system based on a wireless sensor network is proposed. It consists of three parts: data monitoring nodes, data base station and remote monitoring center. This system is suitable for the complex and large-scale water environment monitoring, such as for reservoirs, lakes, rivers, swamps, and shallow or deep groundwaters. This paper is devoted to the explanation and illustration for our new water environment monitoring system design. The system had successfully accomplished the online auto-monitoring of the water temperature and pH value environment of an artificial lake. The system's measurement capacity ranges from 0 to 80 °C for water temperature, with an accuracy of ±0.5 °C; from 0 to 14 on pH value, with an accuracy of ±0.05 pH units. Sensors applicable to different water quality scenarios should be installed at the nodes to meet the monitoring demands for a variety of water environments and to obtain different parameters. The monitoring system thus promises broad applicability prospects.

219 citations

Journal ArticleDOI

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TL;DR: Two applications using dominant current methods for fibre Bragg grating wavelength interrogation are described: hull loads monitoring on an all-composite fast patrol boat and bolt pre-load loss monitoring in a composite beam in conjunction with a state-space modelling data analysis technique.
Abstract: This work first considers a review of the dominant current methods for fibre Bragg grating wavelength interrogation. These methods include WDM interferometry, tunable filter (both Fabry–Perot and acousto-optic) demultiplexing, CCD/prism technique and a newer hybrid method utilizing Fabry–Perot and interferometric techniques. Two applications using these techniques are described: hull loads monitoring on an allcomposite fast patrol boat and bolt pre-load loss monitoring in a composite beam in conjunction with a state-space modelling data analysis technique.

77 citations