Daniel Watzenig

A particle filter approach for tomographic imaging based on different state-space representations

In electrical capacitance tomography (ECT) the main focus is on the reconstruction of distinct objects with sharp transitions between different phases. Being inherently ill-posed, the reconstruction algorithm requires some sort of regularization to stabilize the solution of the inverse problem. However, introducing regularization may counteract the reconstruction of well-defined contours for grid-based methods. Level set propagation approaches which also rely on regularization are able to model sharp phase boundaries but suffer from high computational demands. In this contribution, two different state-space representations of closed contours based on B-splines and on Fourier descriptors are investigated. Both approaches allow us to describe the problem with only a small set of state-space variables. Regularization is incorporated implicitly which can be directly interpreted in the object domain as it relates to smooth contours. To solve the inverse problem, statistical inversion is performed by means of particle filtering providing the opportunity to conveniently incorporate prior information and to take measurement uncertainties into account. The proposed particle filter approach is compared to an extended Kalman filter realization in terms of complexity, computation time and estimation accuracy.

Reconstruction of inhomogeneities in fluids by means of capacitance tomography

Electrical capacitance tomography (ECT) is a technique for reconstructing information about the spatial distribution of the contents of closed pipes by measuring variations in the dielectric properties of the material inside the pipe. In this paper, we propose a method that solves the non‐linear reconstruction problem directly leading to less iterations and higher accuracy than linear back projection algorithms currently in use in most ECT systems.

Statistical robust design using the unscented transformation

Purpose – To establish a statistical formulation of robust design optimization and to develop a fast optimization algorithm for the solution of the statistical design problem.Design/methodology/approach – Existing formulations and methods for statistical robust design are reviewed and compared. A consistent problem formulation in terms of statistical parameters of the involved variables is introduced. A novel algorithm for statistical optimization is developed. It is based on the unscented transformation, a fast method for the propagation of random variables through nonlinear functions. The prediction performance of the unscented transformation is demonstrated and compared with other methods by means of an analytical test function. The validity of the proposed approach is shown through the design of the superconducting magnetic energy storage device of the TEAM workshop problem 22.Findings – Provides a consistent formulation of statistical robust design optimization and an efficient and accurate method fo...

Adaptive regularization parameter adjustment for reconstruction problems

In reconstruction (i.e., determining the states of a model from measurements of model outputs), one is often forced to search for a regularized solution due to poor sensitivity of model outputs with respect to the model states. The amount of regularization is controlled by the regularization parameter, a scalar value multiplied with the so-called regularization term. The choice of the regularization parameter is crucial for the reconstruction process. In this paper, a new method to estimate the regularization parameter in an adaptive way is proposed. A condition-number based estimate of the regularization parameter for the first iteration step is required to choose the weighting factor for adapting the regularization parameter iteratively. By virtue of controlling the regularization term, a kind of edge preservation can be achieved. The validity of this method will be demonstrated for a capacitance tomography problem, which is solved applying a Gauss-Newton scheme.

Accelerated Bayesian Inference for the Estimation of Spatially Varying Heat Flux in a Heat Conduction Problem

This article aims at the acceleration of an inverse heat transfer problem solution within the Bayesian framework. The physical problem involves a spatially varying heat flux, which can reach very large magnitudes in small regions, such as in the heating imposed by high-power lasers. The inverse problem of estimating the imposed heat flux is solved by using the Markov chain Monte Carlo method, with simulated transient temperature measurements. The solution of the inverse problem is based on a reduced model, which consists of an improved lumped formulation of a linearized version of the original nonlinear problem. Two different priors are considered for the sought heat flux, including a total variation density and a Gaussian density. The Gaussian prior is based on the physics of the heat conduction problem. Parameters appearing in both priors are also estimated as part of the inference problem in hyperprior models. The Delayed Acceptance Metropolis-Hastings (DAMH) Algorithm and the Enhanced Approximation Error Model (AEM) are applied with the objective to improve the accuracy of the inverse problem solution.

Engine state monitoring and fault diagnosis of large marine diesel engines

ZusammenfassungIn vielen Industriezweigen ist die zuverlässige Erkennung von Fehlfunktionen im Motor für die Vorhersage und Planung von Wartungsintervallen unabkömmlich. Auf Hochseeschiffen, die sich oft mehrere Monate auf offener See befinden, kann ein Ausfall des Motors zu teuren Standzeiten führen. Zustandsdiagnosesysteme (ZDS) sollten daher in der Lage sein, kostengünstig sowohl den Gesundheitszustand des Motors abzuschätzen als auch Abnützungserscheinungen rechtzeitig erkennen und identifizieren zu können. In diesem Artikel werden zwei verschiedene auf thermodynamischen Modellen basierende Ansätze für die Erkennung von zwei häufig in Großmotoren auftretenden Fehlerursachen – erhöhtes blow-by und Kompressionsverluste – unter Verwendung der Zylinderinnendruckverläufe mit geringer Abtastrate diskutiert. Besonderes Augenmerk liegt dabei auf der Robustheit und Zuverlässigkeit der Parameterschätzung durch Ausblenden der Verbrennungsphase und von Signalteilen mit hohem Rauschpegel. Die vorgestellten Algorithmen werden anhand von realen Messdaten validiert.SummaryThe reliable detection of engine malfunctions in order to predict and to plan maintenance intervals is of major importance in various fields of industry. For instance, occurring faults of marine diesel engines which are on the high seas for months may lead to expensive holding times. In this context, condition monitoring systems (CMS) should be able to assess engine health, to predict developing failures, i.e. engine state degradation, and to diagnose failure modes at a low price. In this article, two different thermodynamical model-based approaches to detect two common failure modes – increased blow-by and compression ratio failures – of large diesel engines given cylinder pressure traces with low sampling rate are discussed and compared. Special focus is put on estimation robustness and reliability by excluding the combustion phase and signal parts with high noise level. The proposed algorithms are validated with experimental data.

A Dual Mode Ultrasound and Electrical Capacitance Process Tomography Sensor

The tomographic imaging of process parameters in industrial applications can be based on different sensing modalities, e.g. ultrasonic waves and electrical capacitance, all of which are sensitive to specific properties of the materials to be imaged. They have shown to be useful tools for process monitoring in different industries. However, the achievable image quality and resolution are generally limited in practice due to the ill-posedness of the inverse problem and limited available data. To further improve the results, the fusion of ultrasound transmission tomography (UTT) and electrical capacitance tomography (ECT) for monitoring two-phase flows is proposed in this paper. The two methods offer complementary properties, making them well suited for data fusion. The ultrasound image is used as a priori information for the finite element method-based capacitance reconstruction algorithm. A tentative dual-mode sensor and static measurements of gas-solid two-phase material distributions are presented. The results indicate that a significant improvement in image quality can be achieved with the proposed sequential tomography sensor fusion

Current Reconstruction Algorithms in Electrical Capacitance Tomography

Figure 1 depicts a scheme of an electrical capacitance tomography (ECT) sensor. A number of electrodes are mounted on the exterior of a nonconductive process pipe. By measurements of the capacitances between certain electrode, it is aim to compute an image of the material distribution.

Influence of the discretization error on the reconstruction accuracy in electrical capacitance tomography

Purpose – The investigation of the influence of the modeling error on the solution of the inverse problem given uncertain measured data in electrical capacitance tomography (ECT).Design/methodology/approach – The solution of the nonlinear inverse problem in ECT and hence, the obtainable accuracy of the reconstruction result, highly depends on the numerical modeling of the forward map and on the required regularization. The inherent discretization error propagates through the forward map, the solution of the inverse problem, the subsequent calculation of process parameters and properties and may lead to a substantial estimation error. Within this work different finite element meshes are compared in terms of obtainable reconstruction accuracy. In order to characterize the reconstruction results, two error measures are introduced, a relative integral error and the relative error in material fraction. In addition, the influence of the measurement noise given different meshes is investigated from the statistic...

Modelling and analysis of the non-iterative coupling process for co-simulation

Concerning non-iterative co-simulation, stepwise extrapolation of coupling signals is required to solve an overall system of interconnected subsystems. Each extrapolation is some kind of estimation and is directly associated with an estimation error. The introduced disturbance depends significantly on the macro-step size, i.e. the coupling step size, and influences the entire system behaviour. In addition, for synchronization purposes, sampling of the coupling signals can cause aliasing. Instead of analysing the coupling effects in the time domain, as it is commonly practised, we concentrate on a model-based approach to gain more insight into the coupling process. In this work, we consider commonly used polynomial extrapolation techniques and analyse them in the frequency domain. Based on this system-oriented point of view of the coupling process, a relation between the coupling signals and the macro-step size is available. In accordance to the dynamics of the interconnected subsystems, the model-based relation is used to select the most critical parameter, i.e. the macro-step size. Besides a ‘rule of thumb’ for meaningful step-size selection, a co-simulation benchmark example describing a two degree of freedom (2-DOF) mechanical system is used to demonstrate the advantages of modelling and the efficiency of the proposed method.