Adaptive sampling for spatial prediction in environmental monitoring using wireless sensor networks: A review

© 2018 IEEE. The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed

Pulsed Eddy Current Sensing for Condition Assessment of Reinforced Concrete

© 2019 IEEE. Reinforced concrete (i.e., concrete wall-like structures having steel reinforcement rods embedded within) are commonly available as civil infrastructures. Such concrete structures, especially the walls of sewers, are vulnerable to bacteria and gas induced acid attacks which contribute to deterioration of the concrete and subsequent concrete wall loss. Therefore, quantification of concrete wall loss becomes important in determining the health and structural integrity of concrete walls. An effective strategy that can be formulated to quantify concrete wall loss is, locating a reinforcement rod and determining the thickness of concrete overlaying the rod via Non-destructive Testing and Evaluation (NDT E). Pulsed Eddy Current (PEC) sensing is commonly used for NDT E of metallic structures, including ferromagnetic materials. Since steel reinforcement rods that are commonly embedded in concrete also are ferromagnetic, this paper contributes by presenting experimental results, which suggest the usability of PEC sensing for reinforced concrete assessment, via executing the aforementioned strategy

Some convolution and scale transformation techniques to enhance GPR images

© 2019 IEEE. Locating reinforcement rods embedded inside concrete wall-like structures, as well as locating subsurface features such as voids, cracks, and interfaces is an essential part of structural health monitoring of concrete infrastructure. The Ground Penetrating Radar (GPR) technique has been commonly used as a means of Non-destructive Testing and Evaluation (NDT E) which suits the purpose. In the recent past, the interest of using GPR to assess the crowns (i.e., top) of concrete sewers has been rising. Moisture is well known to be a challenge for GPR imaging as moisture tends to influence GPR waves. This challenge becomes more common and persistent inside sewers since sewer walls contain considerable surface and subsurface moisture as a result of the humid environment created by the waste water flowing through sewers as well as the bacteria and gas induced acid attacks. Forming a part of sewer condition assessment-related research with the objective of assessing moist concrete, this paper presents some preliminary results which demonstrate how some simple scale transformations and convolution can help in enhancing GPR images in grey-scale. A set of raw GPR signals captured on a moist concrete block inside a laboratory environment is considered. The effect of enhancement is demonstrated against a benchmark image constructed by mapping the raw signals directly onto grey-scale

Review of Pulsed Eddy Current Signal Feature Extraction Methods for Conductive Ferromagnetic Material Thickness Quantification

Thickness quantification of conductive ferromagnetic materials has become a common necessity in present-day structural health monitoring and infrastructure maintenance. Recent research has found Pulsed Eddy Current (PEC) sensing, especially the detector-coil-based PEC sensor architecture, to effectively serve as a nondestructive sensing technique for this purpose. As a result, several methods of varying complexity have been proposed in recent years to extract PEC signal features, against which conductive ferromagnetic material thickness behaves as a function, in return enabling thickness quantification owing to functional behaviours. It can be seen that almost all features specifically proposed in the literature for the purpose of conductive ferromagnetic material-thickness quantification are in some way related to the diffusion time constant of eddy currents. This paper examines the relevant feature-extraction methods through a controlled experiment in which the methods are applied to a single set of experimentally captured PEC signals, and provides a review by discussing the quality of the extractable features, and their functional behaviours for thickness quantification, along with computational time taken for feature extraction. Along with this paper, the set of PEC signals and some MATLAB codes for feature extraction are provided as supplementary materials for interested readers

Improved signal interpretation for cast iron thickness assessment based on pulsed eddy current sensing

© 2017 IEEE. 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

Design of a lock-in amplifier integrated with a coil system for eddy-current non-destructive inspection

© 2017 IEEE. Eddy-current non-destructive inspections of conductive components are of great interest in several industries including civil infrastructure and the mining industry. In this work, we have used a driver-pickup coil system as the probe to carry out inspection of ferromagnetic plates. The specific geometric configuration of the probe generates weak electric signals that are buried in a noisy environment. In order to detect these weak signals, we have designed and implemented a lock-in amplifier as part of the signal processing technique to increase the signal-to-noise ratio and also improve the sensitivity of the probe. We have used Comsol as a finite element method (FEM) to design the probe and conducted experiments with the probe and the lock-in amplifier. The experimental results, which are in agreement with the FEM results, indicate that the designed probe along with a lock-in amplifier can potentially be used to estimate the thickness of thin plates

Designing a pulsed eddy current sensing set-up for cast iron thickness assessment

© 2017 IEEE. Pulsed Eddy Current (PEC) sensors possess proven functionality in measuring ferromagnetic material thickness. However, most commercial PEC service providers as well as researchers have investigated and claim functionality of sensors on homogeneous structural steels (steel grade Q235 for example). In this paper, we present design steps for a PEC sensing set-up to measure thickness of cast iron, which is unlike steel, is a highly inhomogeneous and non-linear ferromagnetic material. The setup includes a PEC sensor, sensor excitation and reception circuits, and a unique signal processing method. The signal processing method yields a signal feature which behaves as a function of thickness. The signal feature has a desirable characteristic of being lowly influenced by lift-off. Experimental results show that the set-up is usable for Non-destructive Evaluation (NDE) applications such as cast iron water pipe assessment

Non-destructive evaluation of ferromagnetic material thickness using Pulsed Eddy Current sensor detector coil voltage decay rate

© 2018 Elsevier Ltd A ferromagnetic material thickness quantification method based on the decay rate of the Pulsed Eddy Current sensor detector coil voltage is proposed. An expression for the decay rate is derived and the relationship between the decay rate and material thickness is established. Pipe wall thickness estimation is done with a developed circular sensor incorporating the proposed method, and results are evaluated through destructive testing. The decay rate feature has a unique attribute of being lowly dependent on properties such as sensor shape and size, and lift-off, enabling the method to be usable with any detector coil-based sensor. A case study on using the proposed method with a commercial sensor is also presented to demonstrate its versatility

Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling

© 2014 IEEE. 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

Towards Automatic Robotic NDT Dense Mapping for Pipeline Integrity Inspection

This paper addresses automated mapping of the remaining wall thickness of metallic pipelines in the field by means of an inspection robot equipped with Non-Destructive 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. The data gathered represents not only a visual understanding of the condition of the pipe for asset managers, but also constitutes 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