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

A feasability study on guided wave-based robotic mapping

Ultrasonic guided wave imaging techniques have received much attention in recent years for fast screening of large structures. Here we explore the feasibility of Occupancy Grid Mapping (OGM) in order to construct a map of an unknown component for inspection. OGM is a well-established algorithm in robotics, but for the first time we are applying it to Shear Horizontal (SH) guided wave imaging. This approach, in contrast to some existing guided wave-based imaging techniques, would not require prior knowledge of an intact state of the sample. OGM works on the ranges obtained by the Time of Flight (ToF) of the received signals operating in pseudo-pulse-echo mode

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

Characterization of EMAT guided wave reflectivity on welded structures for use in ranging

Guided wave ranging measurements offers an elegant method to localize an inspection robot relative to the geometric features, such as welds, of a structure under test. This paper characterizes the suitability of various EMAT generated guided wave modes when reflecting from butt welds for the purpose of choosing a low frequency mode suitable for accurate ranging. Wave modes were tested in 10mm mild steel plate in experiment and simulation, the method of data extraction is discussed as well as the determination of the wave mode best suited for weld ranging by means of comparison of the reflection coefficients. The authors conclude SH1 at a frequency-thickness product of 2 MHz.mm, is shown to be a highly suitable wave mode for gaining a large reflection from a weld, with an average reflection co-efficient of approximately 0.45 across four different sized weld crowns. A ranging over 1 meter experimentally was demonstrated to have a 2.65% error using our method. This work will enable simultaneous detailed mapping through ranging and inspection of large welded structures by mobile robotic inspection systems using EMAT'

Non-contact ultrasonic-based Bayesian mapping for robotic structural inspection

The state-of-the-art robotic ultrasonic inspection of large structural assets such as oil and gas storage tanks is carried out on a point-by-point scheme, where an ultrasonic probe with a mobile platform is scanned over every point on the component. Point-by-point inspection, which generates a huge amount of data, is time-consuming for inspecting large structural and industrial assets. One way to achieve a more efficient inspection of large structures is to take advantage of ultrasonic guided waves (UGW) suitable for mid-range inspection. Unlike the point-by-point scheme, guided waves can also be used to inspect and detect defects such as corrosions in inaccessible regions (e.g., corrosion under pipe supports). In this ongoing research project, we are working towards simultaneous localisation and mapping (SLAM) of thick structures (~10mm) under inspection using ultrasonic guided waves, in particular shear horizontal (SH) wave modes generated using electromagnetic acoustic transducers (EMATs). In other words, we use guided waves to simultaneously localise the robot and map the geometrical features such as defects and boundaries. We present results on the sensitivity of different guided wave modes for weld detection. We then demonstrate the application of guided wave robotic occupancy grid mapping (GW-OGM) to map internal defects and the unknown structure's edges/boundaries. Both pseudo-pulse-echo and pitch-catch measurement setups are used for this purpose, in which one transducer acts as a transmitter and the other one as a receiver. The former mode is used to localise welded joints, which can be eventually exploited for robot localisation. The latter mode is used for defect identification and characterisation. Defect information such as the depth of defect can be used for predicting the remaining useful life of the component. Furthermore, to create a rich ultrasonic mapping of structures by characterising defects on the fly as the robot navigates the structural assets, we have taken advantage of a machine learning approach to estimate the depth of the corrosion-like defects. Phase-based handcrafted features are extracted and fed into the Gaussian process regression model to estimate the defects' depths using the calibrated simulated data set

Crawler-based automated non-contact ultrasonic inspection of large structural assets

This paper presents an update on the progress of developing a crawler-based automated non-contact ultrasonic inspection system for the evaluation of large structural assets. The system presented is a significant improvement on current robotic NDT crawlers and aims to greatly reduce the time of inspection by creating an internal feature map of the subject in a Simultaneous Localisation And Mapping (SLAM) style method instead of using a lawnmower scanning style where all areas are scanned regardless if they contain features or are featureless. This map will be generated through rapid automated path planning and scanning and will show the location of potential areas of interest, where then, the appropriate method of inspection can be used for a high detailed evaluation. Current and ongoing work presented is as follows; the use of guided waves as the sensory input of an occupancy grid map; evaluating guided wave modes to find the mode most appropriate for this system; minimum thickness estimation using machine learning; improving the transducer setup using a unidirectional transmitter

Towards guided wave robotic NDT inspection : EMAT size matters

The first steps towards fast robotic screening of wall thinning in the industrially relevant example of 10mm thick steel plates are reported. Electromagnetic acoustic transducers (EMATs) are used to generate and detect guided shear horizontal wavemodes, as these show promise for this type of inspection. EMATs are miniaturised to reduce magnetic drag on ferritic steels, and are designed to produce SH0 and SH1wavemodes with 22mm wavelength, which is suitable for testing wall thinning in these samples. Miniaturisation and the resulting reduction of magnetic drag force allows the EMATs to be mounted on a small crawler robot which can then be used to scan the sample/structure

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