Miniaturised SH EMATs for fast robotic screening of wall thinning in steel plates

Electromagnetic acoustic transducers (EMATs) are well suited to generating and detecting a variety of different ultrasonic wavemodes, without the need for couplant, and they can be operated through some coatings. EMATs can be used to generate shear horizontal (SH) waves, which show promise for fast screening of wall thinning and other defects. However, commercial SH-wave EMATs are not suitable for robotic implementation on ferritic steel due to the large magnetic drag force from the magnets. This article describes the design and characterisation of miniaturised SH guided wave EMATs, which significantly reduce the magnetic drag and enable mounting onto a small crawler robot for sample scanning. The performance of the miniaturised EMATs is characterised and compared to a commercial EMAT. It is shown that signal to noise ratio is reduced, but remains within an acceptable range to use on steel. The bandwidth and directivity are increased, depending on the exact design used. Their ability to detect flat bottomed holes mimicking wall thinning is also tested

Miniaturised SH EMATs for fast robotic screening of wall thinning in steel plates

Electromagnetic acoustic transducers (EMATs) are well suited to generating and detecting a variety of different ultrasonic wavemodes, without the need for couplant, and they can be operated through some coatings. EMATs can be used to generate shear horizontal (SH) waves, which show promise for fast screening of wall thinning and other defects. However, commercial SH-wave EMATs are not suitable for robotic implementation on ferritic steel due to the large magnetic drag force from the magnets. This article describes the design and characterisation of miniaturised SH guided wave EMATs, which significantly reduce the magnetic drag and enable mounting onto a small crawler robot for sample scanning. The performance of the miniaturised EMATs is characterised and compared to a commercial EMAT. It is shown that signal to noise ratio is reduced, but remains within an acceptable range to use on steel. The bandwidth and directivity are increased, depending on the exact design used. Their ability to detect flat bottomed holes mimicking wall thinning is also tested

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated

Reconfigurability of Piecewise Affine Systems Against Actuator Faults

Abstract: In this paper, we consider the problem of reconfigurability of peicewise affine (PWA) systems. Actuator faults are considered. A system subject to a fault is considered as reconfigurable if it can be stabilized by a state feedback controller and the optimal cost of the performance of the systems is admissible. Sufficient conditions for reconfigurability are derived in terms of feasibility of a set of Linear Matrix Inequalities (LMIs). The method is implemented on a large scale livestock hybrid ventilation model which was obtained during previous research. 1