Practical Considerations for Sliding Mode Observers for High-Rate Structural Health Monitoring

2018 ◽  
Author(s):  
Bryan Joyce ◽  
Jacob Dodson ◽  
Jonathan Hong ◽  
Simon Laflamme
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sliding Mode ◽  
State Parameter ◽  
High Rate ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Structural Health ◽  
Disturbance Estimation ◽  
Sliding Mode Observers

Structural health monitoring (SHM) of high-rate, mechanical systems in dynamically harsh environments presents many challenges over traditional SHM applications. Damage in these systems must be detected and quantified in tens to hundreds of microseconds in order to have sufficient time to react and mitigate damage. The computation speeds and robustness of sliding mode observers (SMOs) for state, parameter, and disturbance estimation for linear and nonlinear systems make them an attractive approach for real-time SHM of high-rate systems. This paper investigates a novel SMO combined with a recursive least squares parameter estimator to detect and track changing system parameters. The observer is simulated on a one degree-of-freedom system with time-varying model parameters to mimic damage. This paper focuses on practical considerations for SMOs for high-rate systems, such as the effects of measurement noise and sampling rates on the estimator’s accuracy and convergence speeds.

scholarly journals An Experimental Test Bed for Developing High-Rate Structural Health Monitoring Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bryan Joyce ◽  
Jacob Dodson ◽  
Simon Laflamme ◽  
Jonathan Hong
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Experimental Test ◽  
Sliding Mode ◽  
High Rate ◽  
Recursive Least Squares ◽  
Test Bed ◽  
Monitoring Methods ◽  
Structural Health ◽  
External Forces

Complex, high-rate dynamic structures, such as hypersonic air vehicles, space structures, and weapon systems, require structural health monitoring (SHM) methods that can detect and characterize damage or a change in the system’s configuration on the order of microseconds. While high-rate SHM methods are an area of current research, there are no benchmark experiments for validating these algorithms. This paper outlines the design of an experimental test bed with user-selectable parameters that can change rapidly during the system’s response to external forces. The test bed consists of a cantilever beam with electronically detachable added masses and roller constrains that move along the beam. Both controllable system changes can simulate system damage. Experimental results from the test bed are shown in both fixed and changing configurations. A sliding mode observer with a recursive least squares parameter estimator is demonstrated that can track the system’s states and changes in its first natural frequency.

A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring

2020 ◽  
pp. 147592172092064 ◽  
Author(s):  
Cong Zhou ◽  
J Geoffrey Chase
Keyword(s):  
Decision Making ◽  
Structural Health Monitoring ◽  
Hysteresis Loop ◽  
Health Monitoring ◽  
Average Error ◽  
Support Vector ◽  
Model Parameters ◽  
Loop Analysis ◽  
Structural Health ◽  
Vector Machines

Optimizing risk treatment of structures in post-event decision-making is extremely difficult due to the lack of information on building damage/status after an event, particularly for nonlinear structures. This work develops an automated, no human intervention, modeling approach using structural health monitoring results to create accurate digital building clones of nonlinear structures for collapse prediction assessment and optimized decision-making. Model-free hysteresis loop analysis structural health monitoring method provides accurate structural health monitoring results from which model parameters of a nonlinear computational foundation model are identified. A new identifiable nonlinear smooth hysteretic model capturing essential structural dynamics and deterioration is developed to ensure robust parameter identification using support vector machines. Method performance is validated against both numerical and experimental data of a scaled 12-story reinforced concrete nonlinear structure. Results of numerical validation show an average error of 1.5% across 18 structural parameters from hysteresis loop analysis and an average error of 2.0% over 30 identified model parameters from support vector machines in the presence of 10% added root-mean-square noise. Validation using experimental data of the scale test reinforced concrete structure also shows a good match of identified hysteresis loop analysis and predicted nonlinear stiffness changes using the digital clones created with an average difference of 1.4%. More importantly, the predicted response using the digital clones for the highly nonlinear pinched hysteretic behavior matches the measured response well, with the average correlation coefficient Rcoeff = 0.92 and average root-mean-square error of 4.6% across all cases. The overall approach takes structural health monitoring from a tool providing retrospective damage data into automated prospective prediction analysis by “cloning” the structure using computational modeling, which in turn allows optimized decision-making using existing risk analyses and tools.

Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model

2020 ◽  
pp. 147592172097702
Author(s):  
Yi-Ming Zhang ◽  
Hao Wang ◽  
Hua-Ping Wan ◽  
Jian-Xiao Mao ◽  
Yi-Chao Xu
Keyword(s):  
Structural Health Monitoring ◽  
Linear Model ◽  
Health Monitoring ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Monitoring Data ◽  
Model Parameters ◽  
Dynamic Linear Model ◽  
Structural Health ◽  
Log Likelihood

Enormous data are continuously collected by the structural health monitoring system of civil infrastructures. The structural health monitoring data inevitably involve anomalies caused by sensors, transmission errors, or abnormal structural behaviors. It is important to identify the anomalies and find their origin (e.g. sensor fault or structural damage) to make correct interventions. Moreover, online anomaly identification of the structural health monitoring data is critical for timely structural condition assessment and decision-making. This study proposes an online approach for detecting anomalies of the structural health monitoring data based on the Bayesian dynamic linear model. In particular, Bayesian dynamic linear model, consisting of various components, is implemented to characterize the feature of real-time measurements. Expectation maximization algorithm and Kalman smoother are combined to estimate the Bayesian dynamic linear model parameters and generate log-likelihood functions. The subspace identification method is introduced to overcome the initialization issue of the expectation maximization algorithm. The log-likelihood difference of consecutive time steps is then used to determine thresholds without introducing extra anomaly detectors. The proposed Bayesian dynamic linear model-based approach is first illustrated by the simulation data and then applied to the structural health monitoring data collected from two long-span bridges. The results indicate that the proposed method exhibits good accuracy and high computational efficiency and also allows for reconstructing the strain measurements to replace anomalies.

High-Rate Structural Health Monitoring and Prognostics: An Overview

2021 ◽  
pp. 213-217
Author(s):  
Jacob Dodson ◽  
Austin Downey ◽  
Simon Laflamme ◽  
Michael D. Todd ◽  
Adriane G. Moura ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Rate ◽  
Structural Health

scholarly journals Microsecond State Monitoring of Nonlinear Time-Varying Dynamic Systems

2017 ◽  
Author(s):  
Jacob Dodson ◽  
Bryan Joyce ◽  
Jonathan Hong ◽  
Simon Laflamme ◽  
Janet Wolfson
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
High Amplitude ◽  
High Rate ◽  
Space Structures ◽  
Time Varying ◽  
State Monitoring ◽  
Structural Health ◽  
Rate Adaptive

Reliable operation of next generation high-speed complex structures (e.g. hypersonic air vehicles, space structures, and weapons) relies on the development of microsecond structural health monitoring (μSHM) systems. High amplitude impacts may damage or alter the structure, and therefore change the underlying system configuration and the dynamic response of these systems. While state-of-the-art structural health monitoring (SHM) systems can measure structures which change on the order of seconds to minutes, there are no real-time methods for detection and characterization of damage in the microsecond timescales. This paper presents preliminary analysis addressing the need for microsecond detection of state and parameter changes. A background of current SHM methods is presented, and the need for high rate, adaptive state estimators is illustrated. Example observers are tested on simulations of a two-degree of freedom system with a nonlinear, time-varying stiffness coupling the two masses. These results illustrate some of the challenges facing high speed damage detection.

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