@misc{SuawaFogouMeiselJongmannsetal., author = {Suawa Fogou, Priscile and Meisel, Tenia and Jongmanns, Marcel and H{\"u}bner, Michael and Reichenbach, Marc}, title = {Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {9}, issn = {1424-8220}, doi = {10.3390/s22093516}, pages = {17}, language = {en} } @misc{AssafoStaedterMeiseletal., author = {Assafo, Maryam and St{\"a}dter, Jost Philipp and Meisel, Tenia and Langend{\"o}rfer, Peter}, title = {On the Stability and Homogeneous Ensemble of Feature Selection for Predictive Maintenance: A Classification Application for Tool Condition Monitoring in Milling}, series = {Sensors}, volume = {23}, journal = {Sensors}, number = {9}, issn = {1424-8220}, doi = {10.3390/s23094461}, abstract = {Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features not only serve as inputs to the learning models but also can influence further decisions and analysis, e.g., sensor selection and understandability of the PdM system. Hence, before deploying the PdM system, it is crucial to examine the reproducibility and robustness of the selected features under variations in the input data. This is particularly critical for real-world datasets with a low sample-to-dimension ratio (SDR). However, to the best of our knowledge, stability of the FS methods under data variations has not been considered yet in the field of PdM. This paper addresses this issue with an application to tool condition monitoring in milling, where classifiers based on support vector machines and random forest were employed. We used a five-fold cross-validation to evaluate three popular filter-based FS methods, namely Fisher score, minimum redundancy maximum relevance (mRMR), and ReliefF, in terms of both stability and macro-F1. Further, for each method, we investigated the impact of the homogeneous FS ensemble on both performance indicators. To gain broad insights, we used four (2:2) milling datasets obtained from our experiments and NASA's repository, which differ in the operating conditions, sensors, SDR, number of classes, etc. For each dataset, the study was conducted for two individual sensors and their fusion. Among the conclusions: (1) Different FS methods can yield comparable macro-F1 yet considerably different FS stability values. (2) Fisher score (single and/or ensemble) is superior in most of the cases. (3) mRMR's stability is overall the lowest, the most variable over different settings (e.g., sensor(s), subset cardinality), and the one that benefits the most from the ensemble.}, language = {en} } @misc{MeiselMelnikovAlexanderetal., author = {Meisel, Tenia and Melnikov, Anton and Alexander, Adrian and Br{\"a}ndel, Tim and Monsalve, Jorge M. and Kaiser, Bert and Schenk, Haral}, title = {Directivity optimization of MEMS ultrasonic transducers by implementing acoustic horns}, series = {Proceedings of the 24th International Congress on Acoustics, Gyeongju, Republic of Korea, 24-28 October 2022}, journal = {Proceedings of the 24th International Congress on Acoustics, Gyeongju, Republic of Korea, 24-28 October 2022}, pages = {9}, abstract = {The applications of microscopic ultrasonic transducers are often limited due to their non-optimal directivity pattern. The aim of this work is to design passive structures to adapt the directivity pattern to an intended application, e.g. range finder. The horns were designed and optimized using a Finite-Element-Method (FEM) model and manufactured by a conventional 3d-printing technique. The experimental validation was done using a novel MEMS-based ultrasonic transducer based on lateral actuation developed by Fraunhofer IPMS. This transducer type generates sound waves by displacing air inside a 3x3 mm² silicon chip using microscopic sized beams instead of using a diaphragm. This article presents several horn structures that exhibit a pronounced main lobe, inter alia an exponential horn and a folded horn with reduced overall dimensions, both optimized for an operatin g frequency of 40 kHz. We have shown numerically and experimentally that directivity properties of the transducer were significantly improved considering peak pressure, reduction of the side lobes and main lobe width by simple horn geometries. The implementation of the designed horns will enable additional applications where a specific directivity pattern is required. Furthermore, the given results imply th at the presented design strategies can be used to create various directivity patterns.}, language = {en} }