Structural health monitoring is widely applied in industrial sectors as it reduces costs associated with maintenance intervals and manual inspections of damage in sensitive structures, while enhancing their operation safety. A major concern and current challenge in developing “robust” structural health monitoring systems, however, is the impact of uncertainty in the input training parameters on the accuracy and reliability of predictions. The aim of this article is to adapt an advanced statistical pattern recognition technique capable of considering variations in input parameters and arriving at a new structural health monitoring system more immune to the effect of uncertainty. Gaussian processes have been implemented to predict the state of damage in a typical composite airfoil structure. Different covariance functions were evaluated during the training stage of structural health monitoring. Results through a case study showed a remarkable capability of the Gaussian process–based approach to deal with uncertainty in the pattern recognition problem in structural health monitoring of a multi-layer composite airfoil structure. To illustrate robustness advantage of the approach as compared to conventional neural network models, the damage size and location prediction accuracy of the Gaussian process structural health monitoring has been compared to multi-layer perceptron neural networks. Some practical insights and limitations of the approach have also been outlined.

Contouring error has a significant effect on machining accuracy. In this paper, a model predictive contouring error pre-compensation method is presented that performs model-based error pre-compensation with feedback and constraints. The proposed method uses the predicted contouring error without compensation obtained by simulation, as the reference trajectory of the model predictive compensator. It then calculates the compensation signal using a constrained model predictive control algorithm and adds it to the original reference position as the new system input. Experiments were conducted on an X-Y table to compare the proposed method with a traditional cascaded PI controller and two existing contouring error pre-compensation methods. The experimental results show that higher contouring accuracy can be achieved with the proposed method for both sharp corners and free-form curves.

Feedrate optimization for computer numerically controlled (CNC) machine tools is a challenging task that is growing in importance as manufacturing industry demands faster machine tools. The majority of research in this area focusses on optimizing feedrate using modeled process constraints. Some researchers have suggested using measured process parameters instead. The former approach suffers from uncertainties in the modeled process data that is the starting point of the optimization. The latter approach has difficulties achieving high levels of optimality. This study proposes the combination of both modeled and measured process data. To this end, a control architecture is described that allows combining measured and modeled process constraints. Within this architecture, a new algorithm to determine time optimum feedrates using modeled velocity and acceleration constraints is proposed. The new control structure including the novel feedrate optimization algorithm is verified experimentally on a high speed biaxial table.

This study describes a new self-sensing technique for piezoelectric actuators that estimates both displacement and force from voltage and charge measurements. The novelty of this technique lies in the use of a constitutive model with nonlinear capacitance and an exponential hysteresis model. The advantage of the presented self-sensing technique over similar techniques in the literature is that it only requires one parameter identification experiment and its hysteresis model is computationally efficient. The control performance of the self-sensing technique is evaluated in a sensorless displacement and force control structure. Validation experiments are carried out on a two degree of freedom test setup that mimics micro-positioners by generating simultaneously varying displacements and forces. Less than 3% absolute average errors are obtained for both displacement and force.

Interstory drift measurement is a critical parameter for determining earthquake damages in civil engineering structures. It can be measured using global positioning systems (GPS), accelerometers, LVDTs, and so forth. Each of these methods has limitations in terms of accuracy, cost, installation, and so on. In this study, a novel, laser-based in situ interstory drift measurement setup is proposed and tested on a shake table on which the laser sensors were mounted in the frame to measure interstory drift without any reference frame. The in situ measurements are compared with measurements from reference lasers, which are mounted on a frame separate from the structure.