Permanently installed sensors are becoming increasingly ubiquitous, facilitating very frequent in situ measurements and consequently improved monitoring of ‘trends’ in the observed system behaviour. It is proposed that this newly available data may be used to provide prior warning and forecasting of critical events, particularly system failure. Numerous damage mechanisms are examples of positive feedback; they are ‘self-accelerating’ with an increasing rate of damage towards failure. The positive feedback leads to a common time-response behaviour which may be described by an empirical relation allowing prediction of the time to criticality. This study focuses on Structural Health Monitoring of engineering components; failure times are projected well in advance of failure for fatigue, creep crack growth and volumetric creep damage experiments. The proposed methodology provides a widely applicable framework for using newly available near-continuous data from permanently installed sensors to predict time until failure in a range of application areas including engineering, geophysics and medicine.
Potential drop measurements are routinely used in the non-destructive evaluation of component integrity. Potential drop measurements use either direct current (DC) or alternating current (AC), the latter will have superior noise performance due to the ability to perform phase sensitive detection and the reduction of flicker noise. AC measurements are however subject to the skin effect where the current is electromagnetically constricted to the surface of the component. Unfortunately, the skin effect is a function of magnetic permeability, which in ferromagnetic materials is sensitive to a number of parameters including stress and temperature, and consequently in-situ impedance measurements are likely to be unstable. It has been proposed that quasi-DC measurements, which benefit from superior noise performance, but also tend to the skin-effect independent DC measurement, be adopted for in-situ creep measurements for power station components. Unfortunately, the quasi-DC measurement will only tend to the DC distribution and therefore some remnant sensitivity to the skin effect will remain. This paper will present a correction for situations where the remnant sensitivity to the skin effect is not adequately suppressed by using sufficiently low frequency; the application of particular interest being the in-situ monitoring of the creep strain of power station components. The correction uses the measured phase angle to approximate the influence of the skin effect and allow recovery of the DC-asymptotic value of the resistance. tial drop measurements are minimum phase is presented and illustrated on two cases; the creep strain sensor of practical interest and a conducting rod as another common case to illustrate generality. The correction is demonstrated experimentally on a component where the skin effect is manipulated by application of a range of elastic stresses.
Accurate temperature measurement is a crucial aspect of structural health monitoring and prognosis. Conventional temperature measurement devices are either incapable of measuring subsurface temperatures in solids or need to be invasively installed. This study investigates the use of an ultrasonic technique for non-invasive measurement of subsurface temperatures in steel components; the temperature of a point on an inaccessible surface is inferred using a time-of-flight measurement from a transducer placed on an opposing accessible surface. Two different inversion approaches are presented, one named the assumed distribution method and the other named the inverse thermal modelling method. The robustness and accuracy of the two ultrasonic temperature inversion methods are quantitatively assessed via simulations and controlled experiments. It was found that both the assumed distribution and inverse thermal modelling methods demonstrate short thermal response times and are able to track the temperature evolution of inaccessible surfaces. A series of experimental studies show that in the presence of a 15°C difference between the accessible and inaccessible surfaces, the inaccessible surface temperature is typically measured to within better than 2°C with respect to a resistance temperature detector reference measurement. Additionally, the article compares the measurement performance achieved using a deployable electromagnetic acoustic transducer and a permanently installed piezo-electric PZT transducer. The time-of-flight measurements taken using the electromagnetic acoustic transducer system had higher random noise than the PZT system (standard deviations of 0.42 and 0.016 ns, respectively), subsequently leading to higher random noise in the temperature estimates.
There is a growing interest in using permanently installed sensors to monitor for defects in engineering components; the ability to collect real-time measurements is valuable when evaluating the structural integrity of the monitored component. However, a challenge in evaluating the detection capabilities of a permanently installed sensor arises from its fixed location and finite field-of-view, combined with the uncertainty in damage location. A probabilistic framework for evaluating the detection capabilities of a permanently installed sensor is thus proposed. By combining the spatial maps of sensor sensitivity obtained from model-assisted methods and probability of defect location obtained from structural mechanics, the expectation and confidence in the probability of detection (POD) can be estimated. The framework is demonstrated with four sensor-component combinations, and the results show the ability of the framework to characterise the detection capability of permanently installed sensors and quantify its performance with metrics such as the $${\mathrm{a}}_{90|95}$$ a 90 | 95 value (the defect size where there is 95% confidence of obtaining at least 90% POD), which is valuable for structural integrity assessments as a metric for the largest defect that may be present and undetected. The framework is thus valuable for optimising and qualifying monitoring system designs in real-life engineering applications.
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