Passive low frequency RFID for non-destructive evaluation and monitoring

Ph. D ThesisDespite of immense research over the years, defect monitoring in harsh environmental conditions still presents notable challenges for Non-Destructive Testing and Evaluation (NDT&E) and Structural Health Monitoring (SHM). One of the substantial challenges is the inaccessibility to the metal surface due to the large stand-off distance caused by the insulation layer. The hidden nature of corrosion and defect under thick insulation in harsh environmental conditions may result in it being not noticed and ultimately leading to failures. Generally electromagnetic NDT&E techniques which are used in pipeline industries require the removal of the insulation layer or high powered expensive equipment. Along with these, other limitations in the existing techniques create opportunities for novel systems to solve the challenges caused by Corrosion under Insulation (CUI). Extending from Pulsed Eddy Current (PEC), this research proposes the development and use of passive Low Frequency (LF) RFID hardware system for the detection and monitoring of corrosion and cracks on both ferrous and non-ferrous materials at varying high temperature conditions. The passive, low cost essence of RFID makes it an enchanting technique for long term condition monitoring. The contribution of the research work can be summarised as follows: (1) implementation of novel LF RFID sensor systems and the rig platform, experimental studies validating the detection capabilities of corrosion progression samples using transient feature analysis with respect to permeability and electrical conductivity changes along with enhanced sensitivity demonstration using ferrite sheet attached to the tag; (2) defect detection using swept frequency method to study the multiple frequency behaviour and further temperature suppression using feature fusion technique; (3) inhomogeneity study on ferrous materials at varying temperature and demonstration of the potential of the RFID system; (4) use of RFID tag with ceramic filled Poly-tetra-fluoro-ethyulene (PTFE) substrate for larger applicability of the sensing system in the industry; (5) lift-off independent defect monitoring using passive sweep frequency RFID sensors and feature extraction and fusion for robustness improvement. This research concludes that passive LF RFID system can be used to detect corrosion and crack on both ferrous and non-ferrous materials and then the system can be used to compensate for temperature variation making it useful for a wider range of applications. However, significant challenges such as permanent deployment of the tags for long term monitoring at higher temperatures and much higher standoff distance, still require improvement for real-world applicability.Engineering and Physical Sciences Research Council (EPSRC) CASE, National Nuclear Laboratory (NNL)

Modelling and experimental investigation of magnetic flux leakage distribution for hairline crack detection and characterization

The Magnetic Flux Leakage (MFL) method is a well-established branch of electromagnetic Non-Destructive Evaluation (NDE) extensively used to assess the physical condition of ferromagnetic structures. The main research objective of this research work presented in this thesis is the detection and characterization of the MFL distribution caused by rectangular surface and far-surface hairline cracks. It looks at the use of the direct current and pulsed current techniques to investigate the presence of hairline cracks in ferromagnetic steel pipelines, by comparing the Finite Element Modelling (FEM) technique with practical experiments. First, the expected response of an MFL probe scanned across the area of a hairline crack was predicted using the 3D FEM numerical simulation technique. The axial magnetization technique is employed and the characteristics of the surface and far-surface leakage field profile

Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

Modelling and experimental investigation of magnetic flux leakage distribution for hairline crack detection and characterization

The Magnetic Flux Leakage (MFL) method is a well-established branch of electromagnetic Non-Destructive Evaluation (NDE) extensively used to assess the physical condition of ferromagnetic structures. The main research objective of this research work presented in this thesis is the detection and characterization of the MFL distribution caused by rectangular surface and far-surface hairline cracks. It looks at the use of the direct current and pulsed current techniques to investigate the presence of hairline cracks in ferromagnetic steel pipelines, by comparing the Finite Element Modelling (FEM) technique with practical experiments. First, the expected response of an MFL probe scanned across the area of a hairline crack was predicted using the 3D FEM numerical simulation technique. The axial magnetization technique is employed and the characteristics of the surface and far-surface leakage field profile

Radio frequency non-destructive testing and evaluation of defects under insulation

PhD ThesisThe use of insulation such as paint coatings has grown rapidly over the past decades. However, defects and corrosion under insulation (CUI) still present challenges for current non-destructive testing and evaluation (NDT&E) techniques. One of such challenges is the large lift-off introduced by thick insulation layer. Inaccessibility due to insulation leads corrosion and defects to be undetected, which can lead to catastrophic failure. Furthermore, lift-off effects due to the insulation layers reduce the sensitivities. The limitations of existing NDT&E techniques heighten the need for novel approaches to the characterisation of corrosion and defects under insulation. This research project is conducted in collaboration with International Paint®, and a radio frequency non-destructive evaluation for monitoring structural condition is proposed. High frequency (HF) passive RFID in conjunction with microwave NDT is proposed for monitoring and imaging under insulation. The small-size, battery-free and cost-efficient nature of RFID makes it attractive for long-term condition monitoring. To overcome the limitations of RFID-based sensing system such as effective monitoring area and lift-off tolerance, microwave NDT is proposed for the imaging of larger areas under thick insulation layers. Experimental studies are carried out in conjunction with specially designed mild steel sample sets to demonstrate the detection capabilities of the proposed systems. The contributions of this research can be summarised as follows. Corrosion detection using HF passive RFID-based sensing and microwave NDT is demonstrated in experimental feasibility studies considering variance in surface roughness, conductivity and permeability. The lift-off effects introduced by insulation layers are reduced by applying feature extraction with principal component analysis and non-negative matrix factorisation. The problem of thick insulation layers is overcome by employing a linear sweep frequency with PCA to improve the sensitivity and resolution of microwave NDT-based imaging. Finally, the merits of microwave NDT are identified for imaging defects under thick insulation in a realistic test scenario. In conclusion, HF passive RFID can be adapted for long term corrosion monitoring of steel under insulation, but sensing area and lift-off tolerance are limited. In contrast, the microwave NDT&E has shown greater potential and capability for monitoring corrosion and defects under insulation

Current deflection NDE for pipe inspection and monitoring

The detection of corrosion on insulated and/or coated pipes in the oil and gas industry remains a challenge. Routine inspection, which is commonly achieved with in-line tools known as "pigs", is not possible where there is any risk of the pig becoming stuck. There are thousands of kilometers of pipe worldwide deemed ``unpiggable'' whose safety must be ensured using Non-Destructive Evaluation (NDE) external to the pipe if potentially catastrophic failure is to be avoided. Many NDE techniques lack sufficient sensitivity due to the coating thickness producing a high standoff distance between the pipe and the sensor and therefore require costly and time-consuming removal of the coating. A method capable of detecting and/or monitoring of defects (e.g. one-third-wall depth corrosion) while leaving the insulation/coating intact would be highly attractive. This thesis documents the development of a technique in which a low-frequency AC current is directly injected into the pipe at distant locations, and perturbations in the magnetic field caused by "current deflection" around defects are measured using solid-state magnetic sensors. Two methods of applying this novel technique were investigated. Firstly, scanning the sensors to measure perturbations in the field and screen for defects, and secondly, permanently installing sensors outside the pipe for Structural Health Monitoring (SHM). A Finite Element (FE) model has been developed and used to investigate the practical challenges that are faced by the technique and how these may be overcome. The sensitivity of the technique for defect detection by external pipe scanning in a practical scenario has then been evaluated using a model-assisted Probability of Detection (POD) framework that combines the measurements of the signal from an undamaged pipe with synthetic damage profiles and contributions from general corrosion and sensor misalignment. The results indicate that good performance is expected for damage detection by scanning above a typical insulation thickness with just a few amps of injected current. A similar framework has then been used to evaluate the sensitivity of the technique as an SHM solution which suggests excellent corrosion detection performance with the permanent installation of inexpensive magnetic sensors. The technique has potential advantages over competing methods in both scanning and monitoring modes and there are many opportunities for future development.Open Acces

Review of corrosion monitoring and prognostics in offshore wind turbine structures: current status and feasible approaches

As large wind farms are now often operating far from the shore, remote condition monitoring and condition prognostics become necessary to avoid excessive operation and maintenance costs while ensuring reliable operation. Corrosion, and in particular uniform corrosion, is a leading cause of failure for Offshore Wind Turbine (OWT) structures due to the harsh and highly corrosive environmental conditions in which they operate. This paper reviews the state-of-the-art in corrosion mechanism and models, corrosion monitoring and corrosion prognostics with a view on the applicability to OWT structures. Moreover, we discuss research challenges and open issues as well strategic directions for future research and development of cost-effective solutions for corrosion monitoring and prognostics for OWT structures. In particular, we point out the suitability of non-destructive autonomous corrosion monitoring systems based on ultrasound measurements, combined with hybrid prognosis methods based on Bayesian Filtering and corrosion empirical models

Detection of multiple defects based on structural health monitoring of pipeline using guided waves technique

Monitoring and inspecting the health condition and state of the pipelines are significant processes for an early detection of any leaking or damages for avoiding disasters. Although most Non Destructive Test (NDT) techniques are able to detect and locate damage during the maintenance intervals, interrupted services could result in high cost and lots of time consumed. In addition, most NDTs are utilized to detect and locate single damage such as axial crack, circular crack, or vertical crack only. Unfortunately, these NDTs are unable to detect or localize multi-type of damages, simultaneously. In this research, the proposed method utilizes the Structural Health Monitoring (SHM) based on guided wave techniques for monitoring steel pipeline continuously in detecting and locating multi-damages. These multi damages include the circumference, hole and slopping cracks. A physical experimental works as well as numerical simulation using ANSYS were conducted to achieve the research objectives. The experimental work was performed to validate the numerical simulation. An artificial neural network was used to classify the damages into ten classes for each type of damage including circumference, hole and sloping cracks. The obtained results showed that the numerical simulation was in agreement with the experimental work with relative error of less than 1.5%. In addition, the neural network demonstrated a feasible method for classifying the damages into classes with the accuracy ranged from 75% to 82%. These results are important to provide substantial information for active condition monitoring activities