M. Flatscher

Model based monitoring of ice accretion on overhead power lines

Atmospheric ice accretion is a natural phenomenon in cold climate regions. On overhead transmission lines, the ice load can cause severe damage and can cause critical situations for those relying on this infrastructure. Even fatal casualties have been documented due to the lapse of transmission lines. Capacitive sensing has been reported a feasible approach for detecting ice accretion events and autonomous sensor systems have been presented. The automated extracting of information about the state of ice accretion, e.g. the layer thickness, from capacitive measurement readings is a vital part for the usability of such systems. In this paper we report on a model based approach for estimating ice accretion parameters from capacitive sensor readings. We identify critical input parameters and present the design of a surrogate model for a capacitive ice sensor. For this we present a test rig to generate ice in a climate chamber. We finally demonstrate the estimation of ice thickness from measurements.

De-icing system with integrated ice detection and temperature sensing for meteorological devices

We present a de-icing system indented for application with meteorological sensors in harsh environments. The heating unit is implemented as a double-meander structure on a printed circuit board. The double-meander structure allows for measuring capacitance in the differential mode. The presence of ice (and water) leads to an increase of the capacitance and can thus be detected. Additionally, the temperature of the heating unit can be obtained from the temperature dependent variation of the ohmic resistance of the heating structure. With this information the heating unit can be controlled efficiently as excessive heating or heating in the absence of ice can be avoided.

Measurement of complex dielectric material properties of ice using electrical impedance spectroscopy

Atmospheric icing accretion is an immanent phenomenon in cold climate regions. For critical infrastructure (e.g. power lines), ice coverage can cause severe damage to the infrastructure, or lead to limited system performance. A reliable sensing principle to detect ice accretion, i.e. to distinguish between water layers and ice, is required. In this paper we present a measurement approach based on electrical impedance spectroscopy to observe frequency dependent properties of water according to its state of matter. The feasibility for sensor applications is shown by means of experiments.

Coaxial probe for dielectric measurements of aerated pulverized materials

Knowledge about the dielectric properties of materials, i.e. the relative permittivity and the conductivity, is important for various sensing applications and engineering. In pneumatic conveying systems the dielectric properties of the transported good are affected by the transport gas, which aerates the pulverized material, effectively decreasing the permittivity. Traditional probe designs like the open ended coaxial probe are well suited for the dielectric characteristics of liquids, gels and pulverized materials. Yet this probes show shortcomings for diluted materials as the probes do not allow a manipulation of the material. In this work we present a probe design to measure the dielectric properties of aerated pulverized materials. The probe maintains a coaxial design with a dedicated gas injection, enabling the analysis of diluted materials.

Capacitance to digital converter based parallelized multi-channel measurement system

Off-the-shelf capacitance to digital converter (CDC) are widely available application specific integrated circuits (ASIC) to build capacitive sensors. For capacitive measurement systems with an increased number of channels, the application of these CDCs is often limited due to their measurement rate. In this paper we propose a parallelized multi-channel measurement system based on off-the-shelf capacitance to digital converters. We identify possible CDCs for the use in a parallel measurement system and demonstrate the usability of the approach by means of measurement results determined on a realized prototype.

PCA based state reduction for inverse problems using prior information

Purpose Inverse problems are often marked by highly dimensional state vectors. The high dimension affects the quality of the estimation result as well as the computational complexity of the estimation problem. This paper aims to present a state reduction technique based on prior knowledge. Design/methodology/approach Ill-posed inverse problems require prior knowledge to find a stable solution. The prior distribution is constructed for the high-dimensional data space. The authors use the prior distribution to construct a reduced state description based on a lower-dimensional basis, which they derive from the prior distribution. The approach is tested for the inverse problem of electrical capacitance tomography. Findings Based on a singular value decomposition of a sample-based prior distribution, a reduced state model can be constructed, which is based on principal components of the prior distribution. The approximation error of the reduced basis is evaluated, showing good behavior with respect to the achievable data reduction. Owing to the structure, the reduced state representation can be applied within existing algorithms. Practical implications The full state description is a linear function of the reduced state description. The reduced basis can be used within any existing reconstruction algorithm. Increased noise robustness has been found for the application of the reduced state description in a back projection-type reconstruction algorithm. Originality/value The paper presents the construction of a prior-based state reduction technique. Several applications of the reduced state description are discussed, reaching from the use in deterministic reconstruction methods up to proposal generation for computational Bayesian inference, e.g. Markov chain Monte Carlo techniques.

Sensing oil layers in manifolds of small size two stroke engines

Small size two stroke engines are the favorite engine for many types of small working machines like chain saws or lawn mowers etc. The popularity of small motorcycles for inner city transport has brought a renaissance of this type of engine. However, with this trend the two stroke engine has also been reported a dominant source of air pollution in many cities around the globe and any engine has now to be designed with respect to emission regulations. For the two stroke engine the ratio between lubrication oil vs. fuel is a crucial parameter to reduce pollutants. A critical spillover of oil creates an oil accumulation in the manifold. Hence knowledge about the amount of oil and prevailing flow patterns in the manifold is of importance for the design and optimization of two stroke engines. In this paper we report on capacitive sensing of lubrication oil in a manifold. We discuss two different approaches and present simulation and measurement results.

Front-end circuit modeling for low-Z capacitance measurement applications

Applications like electrical capacitance tomography (ECT) rely on capacitance measurements with a good signal to noise ratio. In Low-Z measurement circuitry the measurement frequency can be directly used to increase the signal amplitudes. Simple front-end circuit models consider the behavior of all components by means of lumped capacitors. However, for higher system frequencies and long instrumentation lines, transmission line effects can appear. In this paper we develop a front-end circuit model for Low-Z capacitance measurement systems, which considers wave propagation effects due to transmission lines. We demonstrate the applicability of our modelling approach by means of measurements and show further, that the transmission line characteristic can be used to extend the operational limits of the measurement system by means of pre tuning techniques.