Assessment of structural nonlinearities employing extremes of dynamic responses

Pakrashi, Vikram; Fitzgerald, Paul C.; O’Leary, Michael; Jaksic, Vesna; Ryan, Kevin; Basu, Biswajit

doi:10.1177/1077546316635935

Cited by 9 publications

(10 citation statements)

References 41 publications

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“…There is a direct correlation between the SSU and SSF for accelerometers 1 and 2 and accelerometer 3, and this correlation only deviates outside two normal quantiles in the Q-Q plot and the tails of the distribution beyond 0.05 in the normal probability plot. This is also representative of the ability to use established statistical markers for monitoring features of interest [31] in an edge setup. Previous research has also indicated that such markers can accommodate traditional, as well as new [32], types of sensors.…”

Section: Three-dimensional Plots Of Each Bolt-loosening Scenariomentioning

confidence: 95%

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Buckley

Ghosh

Pakrashi

2021

Sensors

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Effective Structural Health Monitoring (SHM) often requires continuous monitoring to capture changes of features of interest in structures, which are often located far from power sources. A key challenge lies in continuous low-power data transmission from sensors. Despite significant developments in long-range, low-power telecommunication (e.g., LoRa NB-IoT), there are inadequate demonstrative benchmarks for low-power SHM. Damage detection is often based on monitoring features computed from acceleration signals where data are extensive due to the frequency of sampling (~100–500 Hz). Low-power, long-range telecommunications are restricted in both the size and frequency of data packets. However, microcontrollers are becoming more efficient, enabling local computing of damage-sensitive features. This paper demonstrates the implementation of an Edge-SHM framework through low-power, long-range, wireless, low-cost and off-the-shelf components. A bespoke setup is developed with a low-power MEM accelerometer and a microcontroller where frequency and time domain features are computed over set time intervals before sending them to a cloud platform. A cantilever beam excited by an electrodynamic shaker is monitored, where damage is introduced through the controlled loosening of bolts at the fixed boundary, thereby introducing rotation at its fixed end. The results demonstrate how an IoT-driven edge platform can benefit continuous monitoring.

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Section: Three-dimensional Plots Of Each Bolt-loosening Scenariomentioning

confidence: 95%

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Buckley

Ghosh

Pakrashi

2021

Sensors

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“…During the vibrational motion of the mass cart, the springs are carefully sheared at a particular time instant, in order to simulate a real-time damage scenario. 46 In the present study, two damage instances are created by shearing two different springs at different time instants. The first damage instant, at t = 13 s , reduces the overall stiffness from 0 . 378 to 0 . 303 N / mm (stiffness reduction of around 19 % ).…”

Section: Experimental Verificationsmentioning

confidence: 99%

“…The second break induces a stiffness alteration of around 17 % , by reducing the equivalent stiffness from 0 . 303 to 0 . 249 N / mm at t = 38 s . 46…”

Section: Experimental Verificationsmentioning

confidence: 99%

Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

Bhowmik

Krishnan

Hazra

et al. 2018

Structural Health Monitoring

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A novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Singular Spectral Analysis (RSSA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed in this paper. The acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its original state to contiguous linear /non-linear-states indicating damage. Most work to date deal with algorithms that require windowing of the gathered data that render them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, that motivates the development of the present framework. The response from a single channel is provided as input to the algorithm in real time. The RSSA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Lower order TVAR models are applied on the transformed responses to improve detectability. Numerical simulations performed on a 5-dof nonlinear system and on an SDOF system modeled using a Duffing oscillator under white noise excitation data, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. The method, further validated on results obtained from experiments performed on a cantilever beam subjected to earthquake excitation; a toy cart experiment model with springs attached to either side; demonstrate the efficacy of the proposed methodology as an appropriate candidate for real time, reference free structural health monitoring.

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“…With diverse and novel output-only real-time methods [34] and markers [35] available for structural health monitoring, it is expected that these methods can eventually help tracking and identifying changes and impacts of implementation of passive control options and possible variations in tuning [1]. Finally, recent works on extreme value analyses for structural health monitoring [36] indicate that such aspects can be relevant and applicable to tracking and marking performance of deployed TLCDs.…”

Section: Figure 20 Herementioning

confidence: 99%

Mitigating the structural vibrations of wind turbines using tuned liquid column damper considering soil-structure interaction

Buckley

Watson²,

Cahill

et al. 2018

Renewable Energy

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This paper considers the potential of using a Tuned Liquid Column Damper (TLCD) to reduce structural vibrations of a wind turbine tower. The effect of TLCD on wind turbine towers, including the soil-structure interactions for a monopile foundation was modelled theoretically and scaled laboratory experiments were carried out to validate these results. The tower of the turbine is represented as an Euler beam with a set of springs at the boundary to simulate the soilstructure interaction. TLCD design was carried out using such a model and the reduction in tower vibrations due to the deployment of TLCD was then examined for various loading conditions in the frequency and the time domain. The efficiency of TLCDs for reducing structural vibrations was investigated for tuned and detuned conditions. The response of a smallscale model was simulated along with that of a full-scale turbine and parametric studies around the variations of inputs related to uncertainties were performed. Experiments were carried out on a scaled model turbine to examine the effectiveness of the TLCD. The practicalities of installing a TLCD in a full-scale turbine were examined.

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Assessment of structural nonlinearities employing extremes of dynamic responses

Abstract: Some rights reserved. For more information, please see the item record link above.

Cited by 9 publications

References 41 publications

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

Mitigating the structural vibrations of wind turbines using tuned liquid column damper considering soil-structure interaction

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