skip to main content


Title: Strain predictions at unmeasured locations of a substructure using sparse response-only vibration measurements
Structural health monitoring of complex structures is often limited by restricted accessibility to locations of interest within the structure and availability of operational loads. In this work, a novel output-only virtual sensing scheme is proposed. This scheme involves the implementation of the modal expansion in an augmented Kalman filter. Performance of the proposed scheme is compared with two existing methods. Method 1 relies on a finite element model updating, batch data processing, and modal expansion (MUME) procedure. Method 2 employs a recursive sequential estimation algorithm, which feeds a substructure model of the instrumented system into an Augmented Kalman Filter (AKF). The new scheme referred to as Method 3 (ME-AKF), implements strain estimates generated via Modal Expansion into an AKF as virtual measurements. To demonstrate the applicability of the aforementioned methods, a rollercoaster connection was instrumented with accelerometers, strain rosettes, and an optical sensor. A comparison of estimated dynamic strain response at unmeasured locations using three alternative schemes is presented. Although acceleration measurements are used indirectly for model updating, the response-only methods presented in this research use only measurements from strain rosettes for strain history predictions and require no prior knowledge of input forces. Predicted strains using all methods are shown to sufficiently predict the measured strain time histories from a control location and lie within a 95% confidence interval calculated based on modal expansion equations. In addition, the proposed ME-AKF method shows improvement in strain predictions at unmeasured locations without the necessity of batch data processing. The proposed scheme shows high potential for real-time dynamic estimation of the strain and stress state of complex structures at unmeasured locations.  more » « less
Award ID(s):
1640693
NSF-PAR ID:
10253588
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of Civil Structural Health Monitoring
Volume:
147
Issue:
5
ISSN:
2190-5452
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. High cycle fatigue is a major cause of cracking in steel structures subjected to cyclic loading. It can result in substantial financial losses and structural failures compromising the safety of users. Uniaxial methods are in many cases insufficient for large in-service structures with complex geometry and connections subjected to multiaxial non-proportional loadings. A new method for fatigue life prediction for complex structures is presented using the critical plane method and the Kalman filter. The applicability of the methodology proposed is demonstrated and evaluated in a roller coaster support structure. Strain rosettes and accelerometers were installed on a support bracket near weld lines to measure responses. A substructure model is defined and used to estimate response prediction in the weld of the support bracket. The estimation of the input and the state estimation is performed using the augmented Kalman filter method, based on the response measurements and the substructured model. This new methodology is anticipated to be used for real-time fatigue prognosis of highway bridges. 
    more » « less
  2. This article addresses the problem of dynamic online estimation and compensation of hard-iron and soft-iron biases of three-axis magnetometers under dynamic motion in field robotics, utilizing only biased measurements from a three-axis magnetometer and a three-axis angular rate sensor. The proposed magnetometer and angular velocity bias estimator (MAVBE) utilizes a 15-state process model encoding the nonlinear process dynamics for the magnetometer signal subject to angular velocity excursions, while simultaneously estimating nine magnetometer bias parameters and three angular rate sensor bias parameters, within an extended Kalman filter framework. Bias parameter local observability is numerically evaluated. The bias-compensated signals, together with three-axis accelerometer signals, are utilized to estimate bias-compensated magnetic geodetic heading. Performance of the proposed MAVBE method is evaluated in comparison to the widely cited magnetometer-only TWOSTEP method in numerical simulations, laboratory experiments, and full-scale field trials of an instrumented autonomous underwater vehicle in the Chesapeake Bay, Maryland, USA. For the proposed MAVBE, (i) instrument attitude is not required to estimate biases, and the results show that (ii) the biases are locally observable, (iii) the bias estimates converge rapidly to true bias parameters, (iv) only modest instrument excitation is required for bias estimate convergence, and (v) compensation for magnetometer hard-iron and soft-iron biases dramatically improves dynamic heading estimation accuracy. 
    more » « less
  3. Abstract

    The mode shape is one of the important modal parameters that enables to visualize the intrinsic behavior of a structure as well as the quantity of interest by extracting or separating modal response from measurements. In this study, a new output‐only framework is proposed to extract modes using a modal‐based Kalman filter defined in the modal space and identify the mode shape by manipulating the correlation between the separated modes and the measured responses. It is also shown that the proposed framework can be extended to estimate the mode shapes of a non‐classically damped structure in state space when the state variable is constructed from the measured responses and applied to the modal‐based Kalman filter. The mode shape estimation framework proposed in this study was verified by numerical simulations and full‐scale measurements. From the verification examples and their results, it was noted that the proposed modal identification framework is not influenced by the presence of noise, and it can be applied to identify the state‐space mode shapes of non‐classically damped systems as well as systems with very closely distributed modes such as buildings equipped with tuned mass dampers.

     
    more » « less
  4. Uwe Sauer, Dirk (Ed.)
    A B S T R A C T This paper proposes a model for parameter estimation of Vanadium Redox Flow Battery based on both the electrochemical model and the Equivalent Circuit Model. The equivalent circuit elements are found by a newly proposed optimization to minimized the error between the Thevenin and KVL-based impedance of the equivalent circuit. In contrast to most previously proposed circuit models, which are only introduced for constant current charging, the proposed method is applicable for all charging procedures, i.e., constant current, constant voltage, and constant current-constant voltage charging procedures. The proposed model is verified on a nine-cell VRFB stack by a sample constant current-constant voltage charging. As observed, in constant current charging mode, the terminal voltage model matches the measured data closely with low deviation; however, the terminal voltage model shows discrepancies with the measured data of VRFB in constant voltage charging. To improve the proposed circuit model’s discrepancies in constant voltage mode, two Kalman filters, i.e., hybrid extended Kalman filter and particle filter estimation algorithms, are used in this study. The results show the accuracy of the proposed equivalent with an average deviation of 0.88% for terminal voltage model estimation by the extended KF-based method and the average deviation of 0.79% for the particle filter-based estimation method, while the initial equivalent circuit has an error of 7.21%. Further, the proposed procedure extended to estimate the state of charge of the battery. The results show an average deviation of 4.2% in estimating the battery state of charge using the PF method and 4.4% using the hybrid extended KF method, while the electrochemical SoC estimation method is taken as the reference. These two Kalman Filter based methods are more accurate compared to the average deviation of state of charge using the Coulomb counting method, which is 7.4%. 
    more » « less
  5. This paper presents a Multiplicative Extended Kalman Filter (MEKF) framework using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for Terrain Relative Navigation (TRN) applications. The newly developed velocimeter LIDAR is capable of providing simultaneous position, Doppler velocity, and reflectivity measurements for every point in the point cloud. This information, along with pseudo-measurements from point cloud registration techniques, a novel bulk velocity batch state estimation process and inertial measurement data, is fused within a traditional Kalman filter architecture. Results from extensive emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory and Monte Carlo simulations are presented to evaluate the efficacy of the proposed algorithms. 
    more » « less