Time Series Based Damage Detection and Localization in an Offshore Platform Using Wireless Sensor Networks

2014 ◽  
Author(s):  
Harsh Nandan ◽  
Eric Abrahamson ◽  
Xiangyu Wang ◽  
Carl Brinkmann
Keyword(s):  
Time Series ◽  
Wireless Sensor Network ◽  
Damage Detection ◽  
Sensor Network ◽  
Structural Integrity ◽  
Ar Model ◽  
Wireless Sensor ◽  
Analysis Method ◽  
Stiffness Reduction ◽  
Detection And Localization

Continuous structural integrity monitoring (SIM) can be a valuable complementary tool to the current practice of periodic inspections in detecting damage in jacket platforms. This paper demonstrates the technical feasibility of adopting the recent advances in onshore SIM technology for offshore jacket platforms. Both the analysis method and hardware technology are investigated. To demonstrate the feasibility of the analysis method, a time series based damage detection and localization algorithm is evaluated. Nodal acceleration and brace strain responses from a jacket platform computer model are simulated and used to determine the Autoregressive (AR) model coefficients. Mahalanobis distance calculated from the first 10 AR coefficients is used as the damage feature (DF). The DF’s from three different damage cases comprising of missing member, dented member (stiffness reduction), and cracked member (nonlinear behavior), respectively, are compared with those from the healthy baseline case to detect and localize damage. To demonstrate the feasibility of hardware technology, a survey of the state-of-the-art in wireless sensor network technology is conducted. The survey shows that wireless accelerometers and strain gauges packaged for underwater use can be fitted in a wireless sensor network throughout the jacket using the electromagnetic communication approach. A conceptual configuration of underwater damage detection wireless sensor network for offshore jacket platforms is presented.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

The Application of Wireless Sensor Network in Agriculture Information Collection

2012 ◽  
Vol 263-266 ◽  
pp. 872-877 ◽  
Author(s):  
Ge Lian Song ◽  
Mao Hua Wang ◽  
Xiao Ying ◽  
Rui Yang ◽  
Bin Yun Zhang
Keyword(s):  
Wireless Sensor Network ◽  
Integrated Circuits ◽  
Sensor Network ◽  
Precision Agriculture ◽  
Data Center ◽  
Wireless Sensor ◽  
Information Collection ◽  
Analysis Method ◽  
Laboratory Manual ◽  
Crop Monitoring

It is an important foundation to precision agriculture to collect the influence information of the crop quickly and effectively. The traditional laboratory manual collect and analysis method has been difficult to meet the timeliness requirements of agriculture information collection, through the wireless sensor network to carry on the agriculture information collection is a good way to solve the problem. In this paper, we propose a farmland data acquisition system based on the Wireless Sensor Network technology. The system establishes the whole crop monitoring system through exerting GPRS network and combining integrated circuits, sensors and GPRS communication modules on data transmission. And by using the internet, GPRS and field monitoring communication, the data center can produce a complete record, the curve showing and query of field data, meanwhile it can detect equipment remotely.

scholarly journals A traffic data collection and analysis method based on wireless sensor network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Huan Wang ◽  
Min Ouyang ◽  
Qingyuan Meng ◽  
Qian Kong
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Sensor Network ◽  
Traffic Flow ◽  
Wireless Sensor ◽  
Traffic Data ◽  
Flow Data ◽  
Analysis Method ◽  
Traffic Data Collection ◽  
Data Collection And Analysis

AbstractWith the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis.

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