Seismic-induced damage detection through parallel force and parameter estimation using an improved interacting Particle-Kalman filter

Sen, Subhamoy; Crinière, Antoine; Mevel, Laurent; Cérou, Frédéric; Dumoulin, Jean

doi:10.1016/j.ymssp.2018.03.016

Cited by 15 publications

(16 citation statements)

References 45 publications

(47 reference statements)

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“…The system states are estimated using an IPEnKF algorithm which is a modification of the Interacting Particle-kalman Filter (IPKF) algorithm [8]. With IPEnKF, the response states are estimated using a nested EnKF instead of KF to enable handling the non-linear systems, while an enveloping PF estimates the parameter θ k .…”

Section: System Estimation Using Ipenkf Algorithmmentioning

confidence: 99%

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Damage Detection in Tensegrity Using Interacting Particle-Ensemble Kalman Filter

Aswal

Sen

Mevel

2021

Lecture Notes in Civil Engineering

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Tensegrity structures form a special class of truss with dedicated cables and bars, that take tension and compression, respectively. To ensure equilibrium, the tensegrity members are required to be prestressed. Over prolonged usage, the cables may lose their prestress while bars may buckle, affecting the structural stiffness as well as its dynamic properties. The stiffness of tensegrities also vary with the load even in the absence of damage. This can potentially mask the effect of damage leading to a false impression of tensegrity health. This poses a major challenge in tensegrity health monitoring especially when the load is stochastic and unknown.Present study develops a vibration based output-only time-domain approach for monitoring the health of any tensegrity in the presence of uncertainties due to ambient force and measurement noise. An Interacting Particle Ensemble Kalman Filter (IPEnKF) has been used that can efficiently monitor tensegrity health from contaminated response data. IPEnKF combines a bank of Ensemble Kalman Filters to estimate response states while running within a Particle Filter envelop that estimates a set of location based health parameters. Further to make damage detection cheaper, strain responses are used as measurements. The efficiency of the proposed methodology has been demonstrated using numerical experiments performed on a simplex tensegrity.

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Section: System Estimation Using Ipenkf Algorithmmentioning

confidence: 99%

“…All of the filtering steps are briefly described in the following. The details can be found in the article [8].…”

Section: System Estimation Using Ipenkf Algorithmmentioning

confidence: 99%

Damage Detection in Tensegrity Using Interacting Particle-Ensemble Kalman Filter

Aswal

Sen

Mevel

2021

Lecture Notes in Civil Engineering

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“…There exist successful applications of Bayesian filtering in SHM wherein HIs in θ k are tracked to localize any deterioration in structural health [31,36,38,42]. In the related literature, θ k s are mostly augmented in the state definition as X k = [x k ; θ k ] to estimate them alongside x k [5,[9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…With this approach, a conditional posterior distribution estimation for the system states is followed by a marginal posterior distribution of the system parameters (also known as Rao-Blackwellisation) [4,13,35,38].…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring with non-linear sensor measurements robust to unknown non-stationary input forcing

Sen

Aswal

Zhang

et al. 2021

Mechanical Systems and Signal Processing

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Bayesian filtering based structural health monitoring algorithms typically assume stationary white Gaussian noise models to represent an unknown input forcing. However, typical structural damages occur mostly under the action of extreme loading conditions, like earthquake or high wind/waves, which are characteristically non-stationary and non-Gaussian. Clearly, this invalidates this basic assumption, causing these algorithms to perform poorly under non-stationary noise conditions. This paper extends an existing interacting filtering algorithm to efficiently estimate structural damages while being robust to unknown non-stationary non-Gaussian input forcing. Furthermore, this approach is generalized beyond linear measurements to encompass the case of non-linear measurements such as strains. The joint estimation of state and parameters is performed by combining Ensemble Kalman filtering, for non-linear system state estimation, and Particle filtering to estimate changes in the structural parameters. The robustness against input forcing is achieved through an output injection approach embedded in the state filter equation. Numerical simulations for two kinds of response measurements (acceleration and strain) are performed on a 3D frame structure under different damage location and severity scenarios. The sensitivity with respect to noise and the impact of different sensor combinations have also been investigated.

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“…Similarly, Kalman filtering based method and their implementation is widely used for online damage detection and structural assessment [12], [13]. Kalman filtering based proved to be computational and resource efficient because of their inheritance nature for parallel implementation on resource constraint hardware mainly WSNs [14], [15], [16]. In an effort to reduce the power consumption of the hardware for long-term, continuous operation of the sensor node, event-triggered sensing systems have been developed.…”

Section: Introductionmentioning

confidence: 99%

Multimetric Event-Driven System for Long-Term Wireless Sensor Operation for SHM Applications

et al. 2020

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Wireless sensor networks (WSNs) are promising solutions for large infrastructure monitoring because of their ease of installation, computing and communication capability, and cost-effectiveness. Long-term Civil structural health monitoring (SHM), however, is still a challenge because it requires continuous data acquisition for the detection of random events such as earthquakes and structural collapse. To achieve long-term operation, it is necessary to reduce the power consumption of sensor nodes designed to capture random events and, thus, enhance structural safety. In this paper, we present an event-based sensing system design based on an ultra-low-power microcontroller with programmable event-detection mechanism to allow continuous monitoring; the device is triggered by vibration, strain, or a timer and has a programmed threshold, resulting in ultra-low-power consumption of the sensor node. Furthermore, the proposed system can be easily reconfigured to any existing wireless sensor platform to enable ultra-low power operation. For validation, the proposed system was integrated with a commercial wireless platform to allow strain, acceleration, and time-based triggering with programmed thresholds and current consumptions of 7.43 and 0.85 mA in active and inactive modes, respectively.

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Seismic-induced damage detection through parallel force and parameter estimation using an improved interacting Particle-Kalman filter

Cited by 15 publications

References 45 publications

Damage Detection in Tensegrity Using Interacting Particle-Ensemble Kalman Filter

Damage Detection in Tensegrity Using Interacting Particle-Ensemble Kalman Filter

Structural health monitoring with non-linear sensor measurements robust to unknown non-stationary input forcing

Multimetric Event-Driven System for Long-Term Wireless Sensor Operation for SHM Applications

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