scholarly journals Damage detection and localization algorithm using a dense sensor network of thin film sensors

2017 ◽  
Author(s):  
Austin Downey ◽  
Filippo Ubertini ◽  
Simon Laflamme
Keyword(s):  
Thin Film ◽  
Damage Detection ◽  
Sensor Network ◽  
Localization Algorithm ◽  
Thin Film Sensors ◽  
Detection And Localization

scholarly journals Damage Detection and Localization under Variable Environmental Conditions Using Compressed and Reconstructed Bayesian Virtual Sensor Data

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 306
Author(s):  
Jyrki Kullaa
Keyword(s):  
Damage Detection ◽  
Data Compression ◽  
Sensor Network ◽  
Measurement Data ◽  
Sensor Data ◽  
Extreme Value Statistics ◽  
Virtual Sensors ◽  
Actual Measurement ◽  
Redundant Data ◽  
Detection And Localization

Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayesian virtual sensors only, from which the omitted signals can be reconstructed with higher accuracy than the actual measurement. The selection of the virtual sensors for storage is done individually for each measurement based on the reconstruction accuracy. Data compression and reconstruction for SHM is the main novelty of this paper. The stored and reconstructed signals are used for damage detection and localization in the time domain using spatial or spatiotemporal correlation. Whitening transformation is applied to the training data to take the environmental or operational influences into account. The first principal component of the residuals is used to localize damage and also to design the extreme value statistics control chart for damage detection. The proposed method was studied with a numerical model of a frame structure with a dense accelerometer or strain sensor network. Only five acceleration or three strain signals out of the total 59 signals were stored. The stored and reconstructed data outperformed the raw measurement data in damage detection and localization.

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.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

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