A Quasi-DC Potential Drop Measurement System for Material Testing

Abstract

Potential drop measurements are well established for use in materials testing and are commonly used for crack growth and strain monitoring. Traditionally, the experimenter has a choice between employing direct current (DC) or alternating current (AC), both of which have strengths and limitations. DC measurements are afflicted by competing spurious DC signals and therefore require large measurement currents (10's or 100's of amps) to improve the signal to noise ratio, which in turn leads to significant resistive Joule heating. AC measurements have superior noise performance due to utilisation of phase-sensitive detection and a lower spectral noise density, but are subject to the skin-effect and are therefore not well suited to high-accuracy scientific studies of ferromagnetic materials. In this work a quasi-DC monitoring system is presented which uses very low frequency (0.3-30 Hz) current which combines the positive attributes of both DC and AC while mitigating the negatives. Bespoke equipment has been developed that is capable of low-noise measurements in the demanding quasi-DC regime. A creep crack growth test and fatigue test are used to compare noise performance and measurement power against alternative DCPD equipment. The combination of the quasi-DC methodology and the specially designed electronics yields exceptionally low-noise measurements using typically 100-400 mA; at 400mA the quasi-DC system achieves a 13-fold improvement in signal to noise ratio compared to a 25A DC system. The reduction in measurement current from 25A to 400mA represents a ~3900 fold reduction in measurement power, effectively eliminating resistive heating and enabling much simpler experimental arrangements.

A probabilistic estimation approach for the failure forecast method using Bayesian inference

“…For the specific application of fatigue crack growth, t f (Bayesian) was compared against a statistical model of the "classic" FFM implementation, t f (regression). We first compared the two models on synthetic data based on a real accelerated fatigue test published in [24], and then compared both models' t f estimation made by the models on an unaltered accelerated fatigue test from [34].…”

“…The t f estimations from both models were also compared on data from an un-altered fatigue test from [34]. To account for the non-stationarity often observed in the inverse feature rate data, the Bayesian model was adapted to incorporate an α switch point, allowing for two sets of Θ (Bayesian model parameters) to be sampled before and after the α switch point.…”

“…The feature data was taken from a fatigue experiment using a standard 316 stainless steel compact tension test specimen, detailed in figure (7) and Tables (2) and (3). The feature was monitored using a permanently-installed potential drop measurement system, and the results plotting the rate of normalized resistance (related to crack growth through a simple polynomial) as a function of fatigue cycle are shown in figure (7, right), which also shows the inverse rate of normalized resistance [34]. The measurement in this experiment contains observations throughout the entire crack propagation event, potentially including crack growth stages 1-3, which is…”

Evaluating the Probability of Detection Capability of Permanently Installed Sensors Using a Structural Integrity Informed Approach

“…of order 10 Ω in finite element simulations and a standard deviation of approximately 2.8 nΩ , which is the capability of a state-of-the-art potential-drop system [26].…”

A Non-Intrusive System to Classify the Severity of Damages Caused by Internal Corrosion Using the Potential Drop Technique and Electrical Image Mapping

“…In its range of applications, the measurement of thickness loss and the estimation of crack depth in ducts pipes and process equipment are included. Besides, PD presents suitable results for high-temperature applications and allows online monitoring operation [6,7]. Although it is one of the oldest non-destructive techniques in monitoring corrosion, there are few quantitative results in the literature due to the necessity of mathematical analysis or utilization of computational methods [8,9].…”