Vibration-based structural performance assessment via output-only sub-Nyquist/compressive wireless sensor data

This paper assesses two different approaches for efficient output - only Vibration - based Structural Health Monitoring (V - SHM) in large - scale civil engineering structures, promoting the use of dense arrays of low - power wireless sensors. Firstly, a non - uniform deterministic sub - Nyquist multi - coset sampling scheme is considered to acquire ambient stationary structural response signals. This sampling scheme is coupled with a power spectrum blind sampling technique along with the frequency domain decomposition algorithm of operational modal analysis to obtain structural modal properties. This is accomplished without necessitating either signal reconstruction in the time - domain or signal sparsity assumption . Secondly, a spectro-temporal compressive sensing approach is considered applicable to cases where sign al reconstruction in time - domain is desired. The latter approach considers non-uniform in time random sampling at sub - Nyquist average rates informed by prior knowledge of signal sparsity gained through smart on-sensor operations and sensor/server communication. The usefulness and applicability of two approaches is numerically demonstrated by considering field recorded data pertaining to the monitoring of an overpass open to the traffic and of an operating on-shore wind turbine

Output-only fatigue prediction of uncertain steel structures

A fatigue estimation framework for steel structures is proposed in this study, under realistic assump-tions on the sensor network capacity and under the premise of uncertainty in the structural information available throughout the life-cycle of the monitored structure. To this purpose, in a first step, a joint input-state-parameter estimation problem is formulated, which integrates the dual Kalman filter and the unscented Kalman filter. The former aims at estimating the unknown structural excitations, while the latter solves the state and parameter estimation problem that is closely related to the estimation of stresses in critical areas. Accordingly, in a second step, a fatigue estimator is developed using material fatigue data and damage accumulation rules, which evaluates the stresses at all unmeasured hotspot locations of the structure to the fatigue damage accumulation and prognosis of the remaining fatigue life. Numerical simulations under different measurement setups and available structural properties are presented, in order to demonstrate the method's effectiveness

On the potential of dynamic sub-structuring methods for model updating

While purely data-driven assessment is feasible for the first levels of the Structural Health Monitoring (SHM) process, namely damage detection and arguably damage localization, this does not hold true for more advanced processes. The tasks of damage quantification and eventually residual life prognosis are invariably linked to availability of a representation of the system, which bears physical connotation. In this context, it is often desirable to assimilate data and models, into what is often termed a digital twin of the monitored system. One common take to such an end lies in exploitation of structural mechanics models, relying on use of Finite Element approximations. proper updating of these models, and their incorporation in an inverse problem setting may allow for damage quantification and localization, as well as more advanced tasks, including reliability analysis and fatigue assessment. However, this may only be achieved by means of repetitive analyses of the forward model, which implies considerable computational toll, when the model used is a detailed FE representation. In tackling this issue, reduced order models can be adopted, which retain the parameterisation and link to the parameters regulating the physical properties, albeit greatly reducing the computational burden. In this work a detailed FE model of a wind turbine tower is considered, reduced forms of this model are found using both the Craig Bampton and Dual Craig Bampton methods. These reduced order models are then used and compared in a Transitional Markov Chain Monte Carlo procedure to localise and quantify damage which is introduced to the system. © 2019 by DEStech Publications, Inc. All rights reserved

Output-only schemes for joint input-state-parameter estimation of linear systems

The subject of predicting structural response, for control or fatigue assessment purposes, via output only vibration measurements is an emerging topic of Structural Health Monitoring. The subject of estimation of the states of a partially observed dynamic system within a stochastic framework has been studied by many scientists and there are well developed algorithms to manage both linear and nonlinear state-space models. Dealing with structural systems, the system states comprise the response displacements and velocities at the degrees of freedom of the structure. On one hand, in practical cases it is difficult or sometimes impossible to measure structural displacements and velocities for continuous monitoring purposes. On the other hand, recent developments in highly accurate low consumption wireless MEMS accelerometers permit continuous and accurate acceleration measurements when dealing with structural systems. Dealing with operational conditions the uncertainties stemming from the absence of information on the input force, model inaccuracy and measurement errors render the state estimation a challenging task, with research to achieve a robust solution still in progress. Eftekhar Azam et al. [1] have proposed a novel dual Kalman filter to accomplish the task of joint input-state estimation for linear time invariant systems. In this study, the extension of such a scheme is considered for the joint input-state and parameter estimation of linear systems

Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating

In this work, a computational framework is proposed for fatigue damage estimation in structural systems by integrating operational experimental measurements in a high-fidelity, large-scale finite element model. The proposed method is applied in a linear steel substructure of a lignite grinder assembly at a Public Power Corporation power plant. A finite element model of the steel base is developed and updated to match the dynamic characteristics measured in real operating conditions. This is achieved through coupled use of numerical and experimental methods for identifying, updating, and optimizing a high-fidelity finite element model. The full stress time histories of the complex mechanical assembly are estimated, at critical locations, by imposing operational vibration measurements from a limited number of sensors in the updated finite element model. Fatigue damage and remaining lifetime is subsequently estimated via commonly adopted engineering approaches, such as Palmgren–Miner damage rule, S–N curves, and rainflow cycle counting. Incorporation of a numerical model of the structure in the response estimation procedure permits stress estimation at unmeasured locations, thereby enabling the drawing of a complete and substantially dense fatigue map consistent with the vibration measurements. Fatigue predictions via the proposed framework are highly correlated to experimental fatigue results, proving the efficiency and applicability of the framework. © The Author(s) 2018

Computational Framework for Online Estimation of Fatigue Damage using Vibration Measurements from a Limited Number of Sensors

This study proposes a computational framework for the online estimation of fatigue damage using operational vibration measurements from a limited number of sensors. To infer the stress response time histories required for fatigue prediction, the measured structural response is driven to a high fidelity finite element (FE) model, which is reconciled using appropriate model updating techniques that minimize the discrepancy between the experimental and analytical frequency response functions (FRFs). Fatigue is accordingly estimated via the Palmgren-Miner rule, while the available FE model allows for stress estimation at unmeasured spots. The method is successfully validated and assessed through an experimental study that pertains to a linear steel substructure supporting the entire body of a pre-beater assembly at a PPC power plant. © 2017 The Authors. Published by Elsevier Ltd

Computational Framework for Online Estimation of Fatigue Damage using Vibration Measurements from a Limited Number of Sensors

This study proposes a computational framework for the online estimation of fatigue damage using operational vibration measurements from a limited number of sensors. To infer the stress response time histories required for fatigue prediction, the measured structural response is driven to a high fidelity finite element (FE) model, which is reconciled using appropriate model updating techniques that minimize the discrepancy between the experimental and analytical frequency response functions (FRFs). Fatigue is accordingly estimated via the Palmgren-Miner rule, while the available FE model allows for stress estimation at unmeasured spots. The method is successfully validated and assessed through an experimental study that pertains to a linear steel substructure supporting the entire body of a pre-beater assembly at a PPC power plant

Monitoring of the Chillon viaduct after strengthening with UHPFRC

Located at the shore of Geneva Lake, in Switzerland, the Chillon viaducts are two parallel structures consisted of post-tensioned concrete box girders, with a total length of 2 kilometers and 100m spans. Built in 1969, the bridges currently accommodate a traffic load of 50.000 vehicles per day, thereby holding a key role both in terms of historic value as well as socio-economic significance. Although several improvements have been carried out in the past two decades, recent inspections has demonstrated an alkali-aggregate reaction in the concrete deck slab reducing its strength. In order to strengthen the concrete deck and slow down further AAR, a layer of 40 mm of Ultra High Performance Fiber Reinforced cement-based Composite (UHPFRC) (incorporating rebars) was cast over the slabs, acting as a waterproof membrane and providing significant increase in resistance via the UHPFRC - RC composite action, in particular of the deck slab. Two years after completing the works, a Structural Monitoring campaign was installed on the deck slab in one representative span, based on accelerometers, strain gauges, thermal and humidity sensors. This campaign seeks to reveal information on the behavior of UHPFRC-concrete composite systems, such as increase in stiffness, fatigue strength, durability and long-term performance. Consequently, the campaigns is expected to last for at least three years. A first insight of the analyzed results from the initial months of measurements is presented herein, along with future improvements or necessary changes on the deployment