The operation efficiency and safety of pressure vessels in the oil and gas industry profits from an accurate knowledge about the inner filling distribution. However, an accurate and reliable estimation of the multi-phase height levels in such objects is a challenging task, especially when considering the high demands in practicability, robustness in harsh environments and safety regulations. Most common systems rely on impractical instrumentation, lack the ability to measure solid phases or require additional safety precautions due to their working principle. In this work, another possibility to determine height levels by attenuation tomography with guided elastic waves is proposed. The method uses a complete instrumentation on the outer vessel shell and is based on the energy conversion rates along the travel path of the guided waves. Noisy data and multiple measurements from sparsely distributed sensor networks are translated into filling levels with accuracies in the centimeter range by solving a constrained optimization problem. It was possible to simultaneously determine sand, water, and oil phases on a mock-up scale experiment, even for artificially created sand slopes. The accuracy was validated by artificial benchmarking for a horizontal vessel, giving references for constructing an affordable prototype system.

No abstract

Many technical processes, e.g. in mechanical engineering, are causing acoustic emission. Acoustic emission (AE) consists of elastic waves, generated by stress changes in a solid. These waves can be detected at the surface of the solid by piezoelectric sensors. Classical methods to characterize acoustic emission signals include detecting and counting single events, describing their energy and frequency properties. The spreading conditions for acoustic waves in solids and the interference of a large number of AE sources lead to quasi-continuous signals from which no individual AE event can be extracted. This is also typical for wire sawing. If AE signals shall be used for online process monitoring, it is necessary to extract signal properties that are correlated with process changes. A common feature is the RMS value of the signal, which is correlated with the energy of AE and was found to be very sensitive to changing process conditions. Other features used are the peak values of the signal and the number of zero crossings. To get more information about the actual state of the observed process, parameters of the statistical distribution of short-time RMS like mean value, variation coefficient and skewness have been tested and their sensitivity to process changes have been investigated. An online monitor has been developed based on a hard- and software concept, adapted to process continuous acoustic emission data, with fast acquisition rates and signal processing

No abstract

In this contribution we study vibration testing for ceramic parts on the example of an electrolyte cup, used in a prospective power cells design. An adapted experimental arrangement for the vibration excitation and the acoustic measurements was built and tested. In parallel, extensive numerical modal analysis simulations were performed using ANSYS. The resonance spectra obtained by modelling agree with the experimentally determined spectra in such a way that the experimentally determined eigenfrequencies can be assigned to the cup modes. The correctness of this identification was verified by direct mode visualization with scanning laser doppler vibrometry. A much faster and potentially in-line capable method for experimental mode identification is the simultaneous measurement at several points using a microphone array and subsequent signal evaluation with operational modal analysis. This procedure was successfully tested. Features in the spectra connected with the presence of flaws are discussed. These features include the drop of some eigenfrequencies and the splitting of degenerated modes.