Multiphysics Structured Eddy Current and Thermography Defects Diagnostics System in Moving Mode

Eddy current testing (ET) and eddy current thermography (ECT) are both important non-destructive testing (NDT) methods that have been widely used in the field of conductive materials evaluation. Conventional ECT systems have often employed to test static specimens eventhough they are inefficient when the specimen is large. In addition, the requirement of high-power excitation sources tends to result in bulky detection systems. To mitigate these problems, a moving detection mode of multiphysics structured ET and ECT is proposed in which a novel L-shape ferrite magnetic yoke circumambulated with array coils is designed. The theoretical derivation model of the proposed method is developed which is shown to improve the detection efficiency without compromising the excitation current by ECT. The specimens can be speedily evaluated by scanning at a speed of 50-250 mm/s while reducing the power of the excitation current due to the supplement of ET. The unique design of the excitation-receiving structure has also enhanced the detectability of omnidirectional cracks. Moreover, it does not block the normal direction visual capture of the specimens. Both numerical simulations and experimental studies on different defects have been carried out and the obtained results have shown the reliability and detection efficiency of the proposed system

Investigation of Infrared Thermography NDE Techniques for Use in Power Station Environments

Three active thermal methods capable of detecting surface breaking cracks in metals are considered in this Thesis. The three thermal methods exploit different means of excitation, each with practical advantages and varying abilities to detect specific types of crack morphology. Thermosonics uses a broadband, high power ultrasonic input to vibrate the test-piece. Defects damp the vibrational energy into heat which is imaged by a thermal camera. Laser-spot thermography uses a short laser pulse to spot heat the surface of the test-piece, and the subsequent radial heat diffusion is then observed. Defects can cause both increased emission of infrared and localised increases in thermal impedance, both effects causing distortion of the radial heat diffusion. Eddy-current induced thermography uses a high power magnetic field to induce a flow of current inside the test-piece. Defects create a localised increase in electrical impedance, diverting the electric field around the defect. This diversion of current flow causes neighbouring regions of high and low current density, the corresponding Joule heating imaged by a thermal camera. In this Thesis the three methods are explored experimentally. For laser-spot thermography and eddy-current induced thermography the physical phenomena are characterised and experimental best-practice for short pulse excitation determined. The effect of crack opening on each of the three methods is found to give insight into which applications the methods are most suited. It was found that the relationship between crack opening and detectability was complex for thermosonics, relatively linear for laser-spot thermography, and that eddy-current induced thermography is largely insensitive to crack opening. The methods are tested for the feasibility of detecting cracks in Inconel buried beneath metallic and ceramic coatings typical of gas turbine blades, with thermosonics and eddy-current induced thermography found to be viable methods. A study of the detectability of a large number of cracks in steel, titanium and Waspaloy by eddy-current induced thermography is detailed, and from this data the probability of detection is established. Eddy-current thermography is shown to be an extremely sensitive method capable of detecting fatigue cracks of approximately 0.25 mm in steel and 0.50-0.75 mm in titanium and Waspaloy. The practicality of the thermal methods is discussed, and the methods put into the context of the wider field of NDE. Based on the works in this Thesis it was found that for most applications eddy-current induced thermography is the most appealing thermal method since it is highly sensitive, rapid, non-contacting and relatively easy to validate. However, both thermosonics and laser-spot thermography remain useful alternative inspections for more niche applications

Sensor Fusion for Electromagnetic Stress Measurement and Material Characterisation

Detrimental residual stresses and microstructure changes are the two major precursors for future sites of failure in ferrous steel engineering components and structures. Although numerous Non-Destructive Evaluation (NDE) techniques can be used for microstructure and stress assessment, currently there is no single technique which would have the capability to provide a comprehensive picture of these material changes. Therefore the fusion of data from a number of different sensors is required for early failure prediction Electromagnetic (EM) NDE is a prime candidate for this type of inspection, since the response to Electromagnetic excitation can be quantified in several different ways: e.g. eddy currents, Barkhausen emission, flux leakage, and a few others. This chapter reviews the strengths of different electromagnetic NDE methods, provides an analysis of the different sensor fusion techniques such as sensor physical system fusion through different principles and detecting devices, and/or feature selection and fusion, and/or information fusion. Two sensor fusion case studies are presented: pulsed eddy current thermography at sensor level and integrative electromagnetic methods for stress and material characterisation at feature (parameters) level

Electromagnetic Thermography Nondestructive Evaluation: Physics-based Modeling and Pattern Mining

Electromagnetic mechanism of Joule heating and thermal conduction on conductive material characterization broadens their scope for implementation in real thermography based Nondestructive testing and evaluation (NDT&E) systems by imparting sensitivity, conformability and allowing fast and imaging detection, which is necessary for efficiency. The issue of automatic material evaluation has not been fully addressed by researchers and it marks a crucial first step to analyzing the structural health of the material, which in turn sheds light on understanding the production of the defects mechanisms. In this study, we bridge the gap between the physics world and mathematical modeling world. We generate physics-mathematical modeling and mining route in the spatial-, time-, frequency-, and sparse-pattern domains. This is a significant step towards realizing the deeper insight in electromagnetic thermography (EMT) and automatic defect identification. This renders the EMT a promising candidate for the highly efficient and yet flexible NDT&E

Service Knowledge Capture and Reuse

The keynote will start with the need for service knowledge capture and reuse for industrial product-service systems. A novel approach to capture the service damage knowledge about individual component will be presented with experimental results. The technique uses active thermography and image processing approaches for the assessment. The paper will also give an overview of other non-destructive inspection techniques for service damage assessment. A robotic system will be described to automate the damage image capture. The keynote will then propose ways to reuse the knowledge to predict remaining life of the component and feedback to design and manufacturing

Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

With the increasing use of induction thermography (IT) for non-destructive testing (NDT) in the mechanical and rail industry, it becomes necessary for the manufactures to rapidly and accurately monitor the health of specimens. The most general problem for IT detection is due to strong noise interference. In order to counter it, general post-processing is carried out. However, due to the more complex nature of noise and irregular shape specimens, this task becomes difficult and challenging. In this paper, a low-rank tensor with a sparse mixture of Gaussian (MoG) (LRTSMoG) decomposition algorithm for natural crack detection is proposed. The proposed algorithm models jointly the low rank tensor and sparse pattern by using a tensor decomposition framework. In particular, the weak natural crack information can be extracted from strong noise. Low-rank tensor based iterative sparse MoG noise modeling is carried out to enhance the weak natural crack information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted for natural crack detection on a variety of specimens. A comparative analysis is presented with general tensor decomposition algorithms. The algorithms are evaluated quantitatively based on signal-to-noise-ratio (SNR) along with the visual comparative analysis

Pixel frequency based railroad surface flaw detection using active infrared thermography for Structural Health Monitoring

Abstract With rapid increase in operation and development of high-speed trains, inspection of railroad surface flaws has become an important aspect for safe and reliable operation of rail network. Non-destructive testing using active infrared thermography has been useful in determining the structural health of different structures with additional benefit of robustness in overall inspection system. This study is based on detection of artificial surface flaws on an in-service railroad. Transverse and longitudinal flaws of various dimensions were machined on rough and smooth rail surface. The railroad surface was thermally stimulated to a temperature equivalent to practical conditions. Emitted radiations from rail surface were captured by an infrared camera to detect cracks. Results show a comparison between the surface flaws on rough and smooth rail surface. Subsequently, raw infrared images were post-processed by statistical image improvement to quantitatively analyse the results. Significant change in the frequency distribution of pixel intensity is observed as the flaw size and depth changes giving a clear quantification of crack topology. A comprehensive and inexpensive solution for damage diagnosis will be offered to railway authorities for Structural Health Monitoring (SHM) and NDT by the proposed framework

Eddy current pulsed thermography for non-destructive evaluation of carbon fibre reinforced plastic for wind turbine blades

PhD ThesisThe use of Renewable energy such as wind power has grown rapidly over the past ten years. However, the poor reliability and high lifecycle costs of wind energy can limit power generation. Wind turbine blades suffer from relatively high failure rates resulting in long downtimes. The motivation of this research is to improve the reliability of wind turbine blades via non-destructive evaluation (NDE) for the early warning of faults and condition-based maintenance. Failure in wind turbine blades can be categorised as three types of major defect in carbon fibre reinforced plastic (CFRP), which are cracks, delaminations and impact damages. To detect and characterise those defects in their early stages, this thesis proposes eddy current pulsed thermography (ECPT) NDE method for CFRP-based wind turbine blades. The ECPT system is a redesigned extension of previous work. Directional excitation is applied to overcome the problems of non-homogeneous and anisotropic properties of composites in both numerical and experimental studies. Through the investigation of the multiple-physical phenomena of electromagnetic-thermal interaction, defects can be detected, classified and characterised via numerical simulation and experimental studies. An integrative multiple-physical ECPT system can provide transient thermal responses under eddy current heating inside a sample. It is applied for the measurement and characterisation of different samples. Samples with surface defects such as cracks are detected from hot-spots in thermal images, whereas internal defects, like delamination and impact damage, are detected through thermal or heat flow patterns. For quantitative NDE, defect detection, characterisation and classification are carried out at different levels to deal with various defect locations and fibre textures. Different approaches for different applications are tested and compared via samples with crack, delamination and impact damage. Comprehensive transient feature extraction at the three different levels of the pixel, local area and pattern are developed and implemented with respect to defect location in terms of the thickness and complexity of fibre texture. Three types of defects are detected and classified at those three levels. The transient responses at pixel level, flow patterns at local area level, and principal or independent components at pattern level are derived for defect classification. Features at the pixel and local area levels are extracted in order to gain quantitative information about the defects. Through comparison of the performance of evaluations at those three levels, the pixel level is shown to be good at evaluating surface defects, in particular within uni- directional fibres. Meanwhile the local area level has advantages for detecting deeper defects such as delamination and impact damage, and in specimens with multiple fibre orientations, the pattern level is useful for the separation of defective patterns and fibre texture, as well as in distinguishing multiple defects.Engineering and Physical Sciences Research Council(EPSRC), Frame Programme 7(FP7