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

Abstract

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.

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

“…A detector coil-based PEC sensor architecture is typically composed of two concentrically wound, air-cored, conductive coils [21][22][23][24][25][26][27][28]32,33]. One coil behaves as the exciter, which is excited by a Heaviside step function-like voltage pulse.…”

“…Based on the model in Equation 1, previous works [22,[24][25][26][27][28] all studied extracting signal features to quantify ferromagnetic material thickness, with almost all features having some relationship to the dominant time constant τ. This paper reviews the feature-extraction methods of those works in the sections to come.…”

“…Experimentally captured PEC signals used in this paper to apply feature-extraction methods were collected on grey cast iron, which is a ferromagnetic material commonly found in aged critical water pipes [34]. Using PEC sensing for condition assessment of grey cast iron is currently of great interest [25][26][27][28][29][30][32][33][34], especially in relation to the task of critical pipe condition assessment [25,26], a real-world NDT application. Signals were captured on grey cast iron blocks of 7, 11, 15, and 20 mm thickness, machined out of a critical pipe test-bed in Sydney Australia.…”

“…The main reason for this associated with detector coil-based PEC sensors can be identified as it being possible to extract signal features that are proportional to the diffusion time constant of eddy currents [23] induced inside a ferromagnetic wall-like structure, and this diffusion time constant behaving as a function of the wall thickness [23]. Consequently, several methods for extracting detector coil-based PEC signal features behaving as functions of thickness have been proposed in recent years [22,[24][25][26][27][28]. Some examples for PEC sensing being used for in line inspection of conductive ferromagnetic underground infrastructure are available in References [29,30].…”

“…As per recent advancements, related feature-extraction methods targeted towards ferromagnetic material-thickness quantification can be classified into two categories: (1) fitting a sum of exponentials to the signal [22,24] (those references do not provide conclusions about the method); and (2) fitting a straight line to the later stage of the signal [25][26][27][28]. The latter category of line fitting again becomes twofold: (a) least-squares-based fitting [25][26][27]; and (b) Savitzky-Golay filter-based fitting [28]. It is evident that nowadays there is an increased interest towards fast and automated condition assessment of conductive ferromagnetic pipes [29,30], mainly targeted at pipe-wall thickness quantification.…”