Chromium electroplating from Cr(III) in deep eutectic solvents. - In: Meeting abstracts, ISSN 2151-2043, Bd. MA2020-02 (2020), 59, 2908
https://doi.org/10.1149/MA2020-02592908mtgabs
Electrochemical nucleation of silicon in ionic liquid-based electrolytes. - In: Meeting abstracts, ISSN 2151-2043, Bd. MA2020-01 (2020), 19, 1181
https://doi.org/10.1149/MA2020-01191181mtgabs
Predicting the quality of high-power connector joints with different machine learning methods. - In: 2020 10th International Online Conference Electric Drives Production Conference (EDPC), (2020), insges. 9 S.
State-of-the-art manufacturing processes used in the electric drive production show a high degree of automation and provide a large amount of process data. Often these data remain unused even though they contain potentially valuable process information. The efficient processing and evaluation of these data bears enormous potential for improving the electric drives production, for example with regard to contacting processes. Innovative machine learning (ML) methods already proved to be a powerful tool for big data set evaluation and continuously enter the manufacturing domain. However, the comprehensive and feasible ML application in manufacturing is hindered by the large effort necessary for adequate data preparation. This work lays the foundation for ML application in ultrasonic metal welding and related contacting techniques, which play an important role in the electric drives production. Therefore, on the one hand side, a data pipeline is developed which covers necessary steps of data preparation. On the other hand side, two data sets generated with a validated US metal welding process model are processed with the data pipeline and quality prediction is performed with three different regression methods, which include classical linear regression as well as advanced ML methods as a neural network. For quality prediction, the mean absolute percentage error reaches values as low as 6.9 %.
https://doi.org/10.1109/EDPC51184.2020.9388211
The performance measurement of the 60GHz mmWave module for IoRL network. - In: 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), (2020), insges. 5 S.
As one of the key features in 5G network, Millimeter wave (mmWave) technology can provide the ultra-wide bandwidth to support higher data rate. However, for high frequency band, mmWave signal still suffers from the high pathloss, the multipath fading and the signal blockage issue, especially in the indoor environment. For different application scenarios, the channel conditions and quality of services (QoS) are quite different. Therefore, it is essential to investigate the impact of the mmWave channel on the system performance. This paper investigates and measures the performance of a 60GHz mmWave module that is exploited for the downlink and uplink high data rate transmission in the Internet of Radio-Light (IoRL) project. The coverage area and the throughput of the mmWave module is estimated by measuring the error vector magnitude (EVM) of received signals with different transmitter (TX) and receiver (RX) angles and at different locations in a laboratory. In this paper, the measurement environment and system setup are introduced. After that, the waveform design for the measurement is also discussed. The measurement results show that this 60GHz mmWave module can provide an acceptable performance only in some cases, which restricts its application scenarios.
https://doi.org/10.1109/BMSB49480.2020.9379731
A framework for developing algorithms for estimating propagation parameters from measurements. - In: 2020 IEEE Globecom workshops (GC Wkshps), (2020), insges. 6 S.
A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by sounders at millimeter-wave (mmW) frequencies. To focus on algorithmic performance, an idealized model is proposed for the spatial frequency response of the propagation environment measured by a sounder. The input to the sounder model is a pre-determined set of MPC parameters that serve as the "ground truth". A three-dimensional angle-delay (beamspace) representation of the measured spatial frequency response serves as a natural domain for implementing and analyzing MPC extraction algorithms. Metrics for quantifying the error in estimated MPC parameters are introduced. Initial results are presented for a greedy matching pursuit algorithm that performs a least-squares (LS) reconstruction of the MPC path gains within the iterations. The results indicate that the simple greedy-LS algorithm has the ability to extract MPCs over a large dynamic range, and suggest several avenues for further performance improvement through extensions of the greedy-LS algorithm as well as by incorporating features of other algorithms, such as SAGE and RIMAX.
https://doi.org/10.1109/GCWkshps50303.2020.9367404
Signal generation for switched reluctance motors using parallel genetic algorithms. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 53 (2020), 2, S. 8193-8198
Switched reluctance motors (SRM) are an inherent part in robotics and automation systems where energy and cost efficiency is required. This motor type has no windings and permanent magnets on the rotor which results in a simple and robust structure. However, SRMs require a complex electronic control system to generate a specified number of voltage pulses for each motor phase. This paper presents the signal generation of multiple phases using only one current sensor in an asymmetric half bridge (AHB). In addition to maintain the predetermined phase voltages, sufficient current measurement windows and a minimal current ripple for the individual phases are further optimization criteria for signal generation. The generation of a state vector which controls the individual semiconductor for each motor phase to achieve a required phase voltage and simultaneously fulfill the multi-objective optimization criteria is challenging. Due to the vast number of possible solutions, a genetic algorithm (GA) was used to find state combinations that are suitable for the formulated optimization criteria. The results were discussed and recommendations about the genotype representation and the used genetic operators were given. Interested readers will find detailed information about the software technical implementation using the Global Optimization Toolbox from MATLAB.
https://doi.org/10.1016/j.ifacol.2020.12.2328
Soft sensor design for restricted variable sampling time. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 53 (2020), 2, S. 80-85
Difficult-to-obtain variables in industrial applications have led to the rise of soft sensors, which use prior system information and measurements to estimate these difficult-to-obtain variables. In real systems, the measurements that need to be estimated by a soft sensor are often infrequently measured or delayed. Sometimes, these delays and sampling time are variable in time. Though there are papers considering soft sensors in the presence of time delays and different sampling times, the variation of those parameters has not been considered when evaluating the adequacy of the soft sensors. Therefore, this paper will evaluate the impact of such variations for a data-driven soft sensor and propose modifications of the soft sensor that increase its robustness. The reliability of its estimate will be shown using the Bauer-Premaratne-Durán Theorem. Furthermore, the soft sensor will be simulated applying it to a continuous stirred tank reactor. Simulation showed that the modified soft sensor gives good estimates, whereas the traditional soft sensor gives an unstable estimate.
https://doi.org/10.1016/j.ifacol.2020.12.097
Sensitivity analysis of bias in satellite sea surface temperature measurements. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 53 (2020), 2, S. 764-771
The satellite sea surface temperature (SST) measurement is based on the detection of ocean radiation using microwave or infrared wavelengths within the electromagnetic spectrum. The radiance of individual wavelengths can be converted into brightness temperatures for using in SST determination. The calibration and validation of the determined SST data require reference measurements from in-situ observations. These in-situ observations are from various platforms such as ships, drifters, floats and mooring buoys and require a high measurement accuracy. This paper presents an investigation about the possibility of using a glider as in-situ platform. A glider is a type of autonomous underwater vehicle (AUV) which can log oceanographic data over a period of up to one year by following predetermined routes. In contrast to buoys, a glider allows a targeted investigation of regional anomalies in SST circulations. To assess the quality of SST observations from a glider, logged data from a glider mission in the Atlantic Ocean from 2018 to 2019 and corresponding satellite SST data were used. The influence of variables (e.g. measurement depth, latitude, view zenith angle, local solar time) of the bias between satellite and glider SST data was investigated using sensitivity analysis. A new and efficient distribution-based method for global sensitivity analyzes, called PAWN, was used successfully. Interested readers will find information about its operation principle and the usage for passive observations where only given-data are available.
https://doi.org/10.1016/j.ifacol.2020.12.828
Sensor fault detection for salient PMSM based on parity-space residual generation and robust exact differentiation. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 53 (2020), 2, S. 86-91
An online model-based fault detection and isolation method for salient permanent magnet synchronous motors is proposed using the parity-space approach. Given the polynomial model equations, Buchberger's algorithm is used to eliminate the unknown variables (e.g. states, unmeasured inputs) resulting in analytic redundancy relations for residual generation. Furthermore, in order to obtain the derivatives of measured signals needed by such a residual generator, robust exact differentiators are used. The fault detection and isolation method is demonstrated using simulation of various fault scenarios for a speed controlled salient motor showing the effectiveness of the presented approach.
https://doi.org/10.1016/j.ifacol.2020.12.099
Data quality assessment for system identification in the age of big data and Industry 4.0. - In: IFAC-PapersOnLine, ISSN 2405-8963, Bd. 53 (2020), 2, S. 104-113
As the amount of data stored from industrial processes increases with the demands of Industry 4.0, there is an increasing interest in finding uses for the stored data. However, before the data can be used its quality must be determined and appropriate regions extracted. Initially, such testing was done manually using graphs or basic rules, such as the value of a variable. With large data sets, such an approach will not work, since the amount of data to tested and the number of potential rules is too large. Therefore, there is a need for automated segmentation of the data set into different components. Such an approach has recently been proposed and tested using various types of industrial data. Although the industrial results are promising, there still remain many unanswered questions including how to handle a priori knowledge, over- or undersegmentation of the data set, and setting the appropriate thresholds for a given application. Solving these problems will provide a robust and reliable method for determining the data quality of a given data set.
https://doi.org/10.1016/j.ifacol.2020.12.103