Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure

2006 ◽  
Vol 291 (1-2) ◽  
pp. 349-368 ◽  
Author(s):  
K. Krishnan Nair ◽  
Anne S. Kiremidjian ◽  
Kincho H. Law
Keyword(s):  
Time Series ◽  
Damage Detection ◽  
Localization Algorithm ◽  
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

scholarly journals A Deep Learning-Based Framework for Damage Detection with Time Series

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qun Yang ◽  
Dejian Shen ◽  
Wencai Du ◽  
Weijun Li
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Damage Detection

scholarly journals Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars

2019 ◽  
Vol 11 (12) ◽  
pp. 1428 ◽  
Author(s):  
Yong Jia ◽  
Yong Guo ◽  
Chao Yan ◽  
Haoxuan Sheng ◽  
Guolong Cui ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Continuous Wave ◽  
Coefficient Matrix ◽  
Constant False Alarm Rate ◽  
Localization Algorithm ◽  
Cfar Detection ◽  
Stepped Frequency ◽  
The Cross ◽  
The One ◽  
Detection And Localization

This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to obtain the correlation coefficient matrix. Then, the constant false alarm rate (CFAR) detection is applied to extract the ranges between each target and the two radars, respectively, from the correlation matrix. Finally, the locations of human targets is calculated with the triangulation localization algorithm. This cross-correlation operation mainly brings about two advantages. On the one hand, the cross-correlation explores the correlation feature of target respiratory signals, which can effectively detect all targets with different signal intensities, avoiding the missed detection of weak targets. On the other hand, the pairing of two ranges between each target and two radars is implemented simultaneously with the cross-correlation. Experimental results verify the effectiveness of this algorithm.

Gear Damage Detection Using Chaotic Dynamics Techniques: A Preliminary Study

1995 ◽  
Author(s):  
M. Farid Golnaraghi ◽  
DerChyan Lin ◽  
Paul Fromme
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Correlation Dimension ◽  
Chaotic Dynamics ◽  
Crack Detection ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Nonlinear Time Series Analysis ◽  
Preliminary Study

Abstract This paper is a preliminary study applying nonlinear time series analysis to crack detection in gearboxes. Our investigations show that the vibration signal emerging from a gearbox is chaotic. Appearance of a crack in a gear tooth alters this response and hence the chaotic signature. We used correlation dimension and Lyapunov exponents to quantify this change. The main goal of this study is to point out the great potential of these methods in detection of cracks and faults in machinery.

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