Pulsed Eddy Current (PEC) sensing is used for Non-Destructive Evaluation (NDE) of the structural integrity of metallic structures in the aircraft, railway, oil and gas sectors. Urban water utilities also have extensive large ferromagnetic structures in the form of critical pressure pipe systems made of grey cast iron, ductile cast iron and mild steel. The associated material properties render NDE of these pipes by means of electromagnetic sensing a necessity. In recent years PEC sensing has established itself as a state-of-the-art NDE technique in the critical water pipe sector. This paper presents advancements to PEC inspection in view of the specific information demanded from water utilities along with the challenges encountered in this sector. Operating principles of the sensor architecture suitable for application on critical pipes are presented with the associated sensor design and calibration strategy. A Gaussian process-based approach is applied to model a functional relationship between a PEC signal feature and critical pipe wall thickness. A case study demonstrates the sensor's behaviour on a grey cast iron pipe and discusses the implications of the observed results and challenges relating to this application.

A novel ferromagnetic material thickness quantification method based on the decay rate of a Pulsed Eddy Current sensor detector coil voltage is proposed. An analytical expression for the decay rate is derived and the relationship with respect to material thickness, in particular that of large diameter pipes, is validated through finite element analysis and experiments. The relationship is verified to hold for a range of ferromagnetic materials and subsequently used for wall thickness quantification of in situ pipes. Estimated pipe wall thickness is evaluated after destructive testing and graphitisation removal. Lift-off insensitivity and potential for thickness estimation through nonconducting coatings is discussed.

This paper addresses automated mapping of the remaining wall thickness of metallic pipelines in the field by means of an inspection robot equipped with nondestructive testing (NDT) sensing. Set in the context of condition assessment of critical infrastructure, the integrity of arbitrary sections in the conduit is derived with a bespoke robot kinematic configuration that allows dense pipe wall thickness discrimination in circumferential and longitudinal direction via NDT sensing with guaranteed sensing lift-off (offset of the sensor from pipe wall) to the pipe wall, an essential barrier to overcome in cement-lined water pipelines. A tailored covariance function for pipeline cylindrical structures within the context of a Gaussian Processes has also been developed to regress missing sensor data incurred by a sampling strategy folllowed in the field to speed up the inspection times, given the slow response of the pulsed eddy current electromagnetic sensor proposed. The data gathered represent not only a visual understanding of the condition of the pipe for asset managers, but also constitute a quantative input to a remaining-life calculation that defines the likelihood of the pipeline for future renewal or repair. Results are presented from deployment of the robotic device on a series of pipeline inspections which demonstrate the feasibility of the device and sensing configuration to provide meaningful 2.5D geometric maps. K E Y W O R D S gaussian process, inspection harsh environments, mapping, NDT, pipeline robot 1 | MOTIVATION-A TAXONOMY OF NDT INSPECTION TECHNIQUES Nondestructive testing (NDT) or evaluation (NDE) is extensively used by the energy and water industry to assess the integrity of their network assets, particularly their larger and most critical conduits (generally refered to as those larger than 350 mm in diameter), in their decision-making process leading their renewal/repair/rehabilitation programs. The key advantage of NDT/NDE is that the structure of the asset is not compromised in estimating its condition. The sensing modality to use is strongly influenced by the material of the asset. Grey Cast Iron (CI) pipelines remain the bulk of the buried critical water infrastructure in the developed world as that was the material of choice for mass production with the advent of the Industrial Revolution in the middle of the 18th century (alongside its less brittle relative of Ductile Iron since 1950s), until carbon steel, asbestos cement, or plastic pipelines (PVC) among other materials made them redundant over the years. The nonhomogeneity of the CI produce means that sensing techniques widely used in the (mild) carbon steel networks in the energy pipeline sector, such as ultrasonics or electromagnetic acoustic transducers, are inadequate for CI, and the underlying techniques of most commercial propositions for CI are instead based on either magnetics (e.g., magnetic flux leakage, pulsed eddy current [PEC], and remote field eddy currents), or the study of the propagation of pressure waves in the pipeline...

This paper describes a Gaussian Process based machine learning technique to estimate the remaining volume of cast iron in ageing water pipes. The method utilizes time domain signals produced by a commercially available pulsed Eddy current sensor. Data produced by the sensor are used to train a Gaussian Process model and perform inference of the remaining metal volume. The Gaussian Process model was learned using sensor data obtained from cast iron calibration plates of various thicknesses. Results produced by the Gaussian Process model were validated against the remaining wall thickness acquired using a high resolution laser scanner after the pipes were sandblasted to remove corrosion. The evaluation shows agreement between model outputs and ground truth. The paper concludes by discussing the implications or results and how the proposed method can potentially advance the current technological setup by facilitating real time pipe profiling.

Abstract-This paper presents a novel signal processing approach for computing thickness of ferromagnetic cast iron material, widely employed in older infrastructure such as water mains or bridges. Measurements are gathered from a Pulsed Eddy Current (PEC) based sensor placed on top of the material, with unknown lift-off, as commonly used during non-destructive testing (NDT). The approach takes advantage of an analytical logarithmic model proposed in the literature for the decaying voltage induced at the PEC sensor pick-up coil. An increasingly more accurate and robust algorithm is proven here by means of an Adaptive Least Square Fitting Line (ALSFL) recursive strategy, suitable to recognize the most linear part of the sensor's logarithmic output voltage for subsequent gradient computation, from which thickness is then derived. Moreover, efficiency is also gained as processing can be carried out on only one decaying voltage signal, unlike averaging over multiple measurements as is usually done in the literature. Importantly, the new signal processing methodology demonstrates highest accuracies at the lower thicknesses, a circumstance most relevant to NDT evaluation. Experiments that verify the proposed method in real-world thickness assessment of cast iron material are presented and compared with current practices, showing promising results.