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Dielectric sensing based on capacitive measurement technology is a favourable measurement approach in many industries and fields of application. From an electrical point of view, a coupling capacitance must be measured in the presence of stray capacitances. Different receiver circuit structures have been proposed for the underlying displacement current measurement. Ideally, the sensor assembly is directly connected to the sensor circuitry to minimize the influence with respect to these parasitic capacitances. However, under harsh operating conditions, e.g., at high temperatures, the sensor and the receiver circuit must be separated in order to protect the electronics. Consequently, the receiver circuit and the sensor have to be connected by cables, e.g., coaxial cables. The measurement setup differs significantly from the ideal design with a direct connection. In this paper, we investigate the behaviour of three common measurement circuits for capacitive measurements in instrumentations with cables. We study the interaction between the sensor and the electronics and analyse the operating behaviour of the circuit, as well as the operating states of the amplifiers used. We also address cross-sensitivities in the sensor design due to stray capacitances. The analyses are carried out for different cable lengths and measuring frequencies, and conditions for the usability of the circuit are deduced. In addition to the operational behaviour, we also evaluate the circuits by means of a noise analyses. Based on this analysis, we show a direct comparison of the circuits. The analysis is based on simulation studies, as well as collaborative measurements on test circuits where all circuit parameters are provided. The test circuits are realized with dedicated state-of-the-art circuit elements and, together with the analysis approach and the results, thus provide a basis for future developments. [ABSTRACT FROM AUTHOR]

Atmospheric ice accretion is considered a severe safety issue for technical systems such as communication infrastructure, power lines, blades of wind turbines, and so on in cold climate regions. Among different sensing systems to detect and provide reliable information about the state of ice, capacitive sensing has been reported as a suitable technique. In capacitive sensing, measured capacitances are influenced due to ice accretion. Signal processing for ice sensors requires to estimate parameters for the state of ice from the data. In this paper, we present a model-based estimation approach for capacitive ice sensors. We analyze different electrode topologies for ice sensing and provide a statistical analysis of the sensor behavior. We hereby consider the random nature of natural ice accretion. From simulations, we show how a statistical model for the measurement process can be derived and demonstrate the construction of estimation algorithms within the Bayesian framework. We will demonstrate the capability of our approach for automated ice detection by means of long-term field data showing the ability to estimate ice layers with a precision better than 1 mm. [ABSTRACT FROM AUTHOR]

Measuring the state of batteries and their change over time is essential during research and development. A number of standard test methods are available to determine specific cell parameters, such as capacitance, internal resistance, coulombic efficiency, etc. Although these methods have been in use for a long time, a thorough analysis of the uncertainties associated with them – especially when using high-precision measuring equipment – is still lacking. Therefore, a primary goal of this work is to close this gap. For each method, the results of the uncertainty analysis are divided into a variable part (noise, jitter) and a constant part (absolute accuracy). In addition, the theoretical analysis is accompanied by practical measurement results from a high-precision measurement hardware, demonstrating what is currently possible with state-of-the-art equipment. The constant part of the uncertainty is mainly limited by the used calibration equipment. For the variable part of uncertainty, the situation is somewhat more varied. Using a battery temperature control with a variability of only ± 0. 1 ∘ C , capacity, capacity change and coulombic efficiency still show a strong influence on the prevailing small temperature variations. For the voltage analysis methods (differential capacity and voltage analysis) the predominate factor is voltage noise and drift. • Ratiometric quantities, e.g. Δ C or CE, reach uncertainties in the low ppm range. • Non-ratiometric quantities, e.g. capacitance, are much worse at several 100 ppm. • The value-to-value jitter is very low, ranging from a few to a few tens of ppm. • For low uncertainty, a highly stable temperature control (few mK) is necessary. [ABSTRACT FROM AUTHOR]