Many damage detection and system identification approaches benefit from the availability of both acceleration and displacement measurements. This is particularly true in the case of suspected nonlinear behavior and permanent deformations. In civil and mechanical structural modeling accelerometers are most often used, however displacement sensors, such as non-contact optical techniques as well as GPS-based methods for civil structures are becoming more common. It is suggested, where possible, to exploit the inherent redundancy in the sensor information and combine the collocated acceleration and displacement measurements in a manner which yields highly accurate motion data. This circumvents problematic integration of accelerometer data that causes lowfrequency noise amplification, and potentially more problematic differentiation of displacement measurements which amplify high-frequency noise. Another common feature of displacement based sensing is that the high frequency resolution is limited, and often relatively low sampling rates are used. In contrast, accelerometers are often more accurate for higher frequencies and thus higher meaningful sampling rates are often available. The fusion of these two data types must therefore combine data sampled at different frequencies. A multi-rate Kalman filtering approach is proposed to solve this problem. In addition, a smoothing step is introduced to obtain improved accuracy in the displacement estimate when it is sampled at lower rates than the corresponding acceleration measurement. Through trials with simulated data the procedure's effectiveness is shown to be quite robust at a variety of noise levels and relative sample rates for this practical problem.
This study investigates the possibility of injecting parametric
features into nonparametric identification techniques like neural
networks in modeling nonlinear dynamic restoring forces. This
affords the potential of creating relationships between model
parameters in data-driven techniques and phenomenological
behaviors in physics-based modeling, which is prompted by the
needs in structural health monitoring and damage detections. Here
a linear sum of sigmoidal basis functions is used in modeling
nonlinear hysteretic restoring forces of single-degree-of-freedom
oscillators under the force-state mapping formulation to showcase
this idea. A constructive approach is proposed to guide the neural
network initial design, where the number of hidden layers and
hidden nodes as well as the initial values of the weights and
biases are decided upon the characteristics of the nonlinear
restoring force to be modeled rather than through indiscriminate
numerical initialization schemes. Numerical simulations are
presented to demonstrate the efficiency and engineered feature of
this approach. A training example is provided to show that this
approach enables neural networks to carry either physical or
phenomenological "meaning" while remaining adaptive and thus
powerful in system identification.
A powerful Volterra/Wiener Neural Network (VWNN) is designed to
reflect the underlying dynamics of hysteretic systems. The
nonlinear response of multi-degree-of-freedom systems subjected to
force excitation can be tracked using this neural network. More
importantly, the inner-workings of the network, such as the design
parameters as well as the weights and biases, can be loosely
related to physical properties of dynamic systems. This effort
differs markedly from what is typically done for neural networks
as well as the original version of the VWNN in Ref. 1. An adaptive training algorithm and improved formulation of high-order nodes are adopted to enable fast training and stable convergence. A training example is provided to demonstrate that the VWNN is able to yield a unique set of solutions (i.e., the weights) when the values of the
controlling design parameters are fixed a priori. The
selection of these design parameters in practical applications is
discussed. The advantages of the VWNN illustrate the potential of
applying highly flexible nonparametric identification techniques
in a parametric fashion to suit the needs of structural health
monitoring and damage detections.
Conference Committee Involvement (7)
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
8 March 2010 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
9 March 2009 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
10 March 2008 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
19 March 2007 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
27 February 2006 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
7 March 2005 | San Diego, California, United States
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
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