Abstract
This study evaluates input error function observers for tracking of stiffness variation in real-time. The input error function is an Analytical Redundancy (AR)-based diagnosis method and necessitates a mathematical model of the system and system identification techniques. In practice, mathematical models used during numerical simulations differ from the actual status of the structure, and thus, accurate mathematical models are rarely available for reference. Noise is an unwanted signal in the input–output measurements but unavoidable in real-world applications (as in long span bridge trusses) and hard to imitate during numerical simulations. Simulation data from the truss system clearly indicates the effectiveness of the proposed structural damage detection method for estimating the severity of the damage. Optimization of the input error function can further automate the stiffness estimation in structural members and address critical aspects such as system uncertainties and the presence of noise in input–output measurements. Stiffness tracking in one of the planar truss members indicates the potential of optimization of the input error function for online structural health monitoring and implementing condition-based maintenance.
Highlights
This paper introduces input error function-based observers for the tracking of stiffness degradation in structural members
A modified formulation of the actuator failure detection algorithm results in a unique input error function corresponding to an individual structural member and makes it convenient for developing a bank of observers to estimate the severity of damages in structural members
Stiffness estimation in a damaged structural member can be automated by implementing an optimization algorithm such that an error function for an individual structural member is minimized
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Due to the unique input error function for each individual structural member, the proposed algorithm in this study overcomes the limitation of a large number of ARMarkov observers, system uncertainties, and noisy output measurements. The proposed methodology in this paper is envisioned for its application to real-time structural health monitoring of smart structures composed of CNT embedded polymers such as structural material as well as wireless, self-sensing CNT sensors. Have studied in situ AE measurements from notched aluminum specimens to detect and track the propagation of the crack under cyclic loading and proposed quantitative methods using AE measurements for quantitative Structural Health Monitoring (SHM). The current study is an attempt to provide one of such algorithms for real-time structural health monitoring and condition-based maintenance.
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