Key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or environmental changes. This paper presents a new approach to account for these changes by updating the system models. Current iterative methods developed to solve the model updating problem rely on minimisation techniques to find the set of model parameters that yield the best match between experimental and analytical responses. These minimisation procedures require considerable computation time, making the existing techniques infeasible for some applications, such as in an adaptive control scheme, correcting the model parameters as the system changes. The proposed approach uses frequency domain data and a neural network to estimate the updated parameters quickly, yielding a model representative of the measured data. Besides control-related applications, this may also be of use for manufacturing systems, where parameters change during operation requiring repeated updates of the nominal model. Numerical simulations and experimental results show that the neural network updating method (NNUM) has good accuracy and generalisation properties, and it is therefore a suitable alternative for the solution of the model updating problem of this class of systems.
1Cost-effective and reliable damage detection is critical for the utilization of composite materials. This paper presents part of an experimental and analytical survey of candidate m ethods for in-situ damage detection of composite materials. Experimental results are presented for the application of Lamb wave techniques to quasi-isotropic graphite/epoxy thin coupons and sandwich beams containing representative damage modes, including delamination, transverse ply cracks and through-holes. Optimization experiments provided a procedure capable of easily and accurately determining the presence of damage by monitoring the transmitted waves with piezoceramic sensors (PZT). Lamb wave techniques have been proven to provide more information about damage type, severity and location than previously tested methods, and may prove suitable for structural health monitoring applications since they travel long distances and can be applied with conformable piezoelectric actuators and sensors that require little power.
Accurate models are necessary in critical applications. Key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or environmental changes. This dissertation presents a new approach to update system models, accounting for these changes. The approach uses frequency domain data and a neural network to produce estimates of the parameters being updated, yielding a model representative of the measured data.Current iterative methods developed to solve the model updating problem rely on minimization techniques to find the set of model parameters that yield the best match between experimental and analytical responses. Since the minimization procedure requires a fair amount of computation time, it makes the existing techniques infeasible for use as part of an adaptive control scheme correcting the model parameters as the system changes. They also require either mode shape expansion or model reduction before they can be applied, introducing errors in the procedure. Furthermore, none of the existing techniques has been applied to nonlinear systems.The neural network estimates the parameters being updated quickly and accurately without the need to measure all degrees of freedom of the system. This avoids the use of mode shape expansion or model reduction techniques, and allows for its implementation as part of an adaptive control scheme. The proposed technique is also capable of updating weakly nonlinear systems.Numerical simulations and experimental results show that the proposed method has good accuracy and generalization properties, and it is therefore, a suitable alternative for the solution of the model updating problem of this class of systems. iv I also wish to thank the following people for helping, supporting and encouraging me during these years:
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