Stochastic change detection in uncertain nonlinear systems using reduced-order models: system identification

Abstract

The reliable detection of relatively small changes in the characteristics of monitored systems, which simultaneously involve nonlinear phenomena as well as uncertain parameters, is a challenging problem whose resolution is crucial to the development of practical structural health monitoring methodologies slated for use with complex physical systems. This paper reports the results of a comprehensive experimental study involving an adaptive nonlinear component (an actively controlled magnetorheological damper) that was used to investigate the representation and propagation of uncertainties in a probabilistic format that provides a convenient means for reliable detection of small changes in uncertain nonlinear systems. In experimental studies of the MR damper, the uncertainty of the system characteristics was precisely controlled with known input-current statistics. A total of 4000 tests were performed, and the MR damper was identified using the restoring force method with both orthogonal and non-orthogonal basis functions. The identification results show that the identified coefficients involving orthogonal basis functions have several desirable features that are ideal for condition assessment purposes when dealing with complex nonlinear systems whose underlying physics is not amenable to easy modeling: (1) no a priori knowledge of the systems characteristics is required; (2) the orthogonal coefficients are statistically unbiased with respect to model complexity; and (3) the distributions of the orthogonal coefficients can be reliably used to detect changes in uncertain nonlinear systems, even if a reduced-order model is used in the identification process.

Nonlinear multiclass support vector machine–based health monitoring system for buildings employing magnetorheological dampers

“…Another important issue to be addressed is that it is critical to maintain the MR damper temperature because the dissipative energy by the MR damper operation increases significantly with increasing temperature of MR fluids during testing. More detailed description can be found in Yun and Masri (2008).…”

“…Damage is simulated by the stiffness decrease at the first- and second-floor levels. Although a variety of damage scenarios need to be considered, for example, stiffness, structural damping coefficients, sensor faults (Sharifi et al, 2010), and damper faults (Yun and Masri, 2008, 2009), we only considered the stiffness degradation. Other factors will be taken into account in our future studies.…”

“…Yun and Masri (2008, 2009) have provided an application example on how nonlinear smart control devices can be monitored. For example, they proposed an effective method for damage detection of magnetorheological (MR) dampers using the restoring force method (RFM).…”

A structural decomposition approach for detecting, locating, and quantifying nonlinearities in chain-like systems

“…19, low‐order fits are desired since the main goal of the identification process is to construct reduced‐order models of the elements in the system. By virtue of the orthogonality property of the estimated Chebyshev coefficients, the optimal model is obtained by truncating the restoring force expansion to a suitable order, which is usually determined through an analysis of the relative‐contribution of the identified Chebyshev coefficients and the normalized mean‐square error 7, 19, 23, 25.…”

A novel deep learning-based method for damage identification of smart building structures

“…Karami and Amini 49 introduced a method based on identified system Markov parameters for alleviating responses and induced damages in the earthquake-excited structures equipped with MR dampers. Yun and Masri 50,51 proposed a practical solution to the monitoring of MR devices based on restoring force method (RFM), in which the orthogonal coefficients are adopted as damage-sensitive feature for DI of MR dampers. Nevertheless, so far, the systematic methods for detecting damages of smart structures with MR devices under ambient loadings are limited.…”