Bio: Thailammai Chitambaram is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Wi-Fi array & Adapter (computing). The author has an hindex of 1, co-authored 1 publications receiving 3 citations.
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.
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.
••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.
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.