Damage Detection and Localization in Structures: A Statistics Based Algorithm Using a Densely Clustered Sensor Network

Author(s):  
Elizabeth L. Labuz ◽  
Shamim N. Pakzad ◽  
Liang Cheng
Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 306
Author(s):  
Jyrki Kullaa

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.


Author(s):  
Harsh Nandan ◽  
Eric Abrahamson ◽  
Xiangyu Wang ◽  
Carl Brinkmann

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.


2018 ◽  
Vol 148 ◽  
pp. 14008 ◽  
Author(s):  
Stanislav Stoykov ◽  
Emil Manoach ◽  
Maosen Cao

The early detection and localization of damages is essential for operation, maintenance and cost of the structures. Because the frequency of vibration cannot be controlled in real-life structures, the methods for damage detection should work for wide range of frequencies. In the current work, the equation of motion of rotating beam is derived and presented and the damage is modelled by reduced thickness. Vibration based methods which use Poincaré maps are implemented for damage localization. It is shown that for clamped-free boundary conditions these methods are not always reliable and their success depends on the excitation frequency. The shapes of vibration of damaged and undamaged beams are shown and it is concluded that appropriate selection criteria should be defined for successful detection and localization of damages.


2016 ◽  
Vol 139 (4) ◽  
pp. 2013-2013 ◽  
Author(s):  
Marco Miniaci ◽  
Anastasiia Krushynska ◽  
Federico Bosia ◽  
Antonio Gliozzi ◽  
Marco Scalerandi ◽  
...  

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