@misc{HoffmannMahmoodSuawaFogouetal., author = {Hoffmann, Javier Eduardo and Mahmood, Safdar and Suawa Fogou, Priscile and George, Nevin and Raha, Solaiman and Safi, Sabur and Schmailzl, Kurt JG and Brandalero, Marcelo and H{\"u}bner, Michael}, title = {A Survey on Machine Learning Approaches to ECG Processing}, series = {2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 23-25 Sept. 2020 , Poznan, Poland}, journal = {2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 23-25 Sept. 2020 , Poznan, Poland}, isbn = {978-83-62065-39-4}, language = {en} } @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{SuawaFogouHalbingerJongmannsetal., author = {Suawa Fogou, Priscile and Halbinger, Anja and Jongmanns, Marcel and Reichenbach, Marc}, title = {Noise-Robust Machine Learning Models for Predictive Maintenance Applications}, series = {IEEE Sensors Journal}, volume = {23}, journal = {IEEE Sensors Journal}, number = {13}, issn = {1558-1748}, doi = {10.1109/JSEN.2023.3273458}, pages = {15081 -- 15092}, abstract = {Predictive maintenance of equipment requires a set of data collected through sensors, from which models will learn behaviors that will allow the automatic detection or prediction of these behaviors. The objective is to anticipate unexpected situations such as sudden equipment stoppages. Industries are noisy environments due to production lines that involve a series of components. As a result, the data will always be obstructed by noise. Noise-robust predictive maintenance models, which include ensemble and deep learning models with and without data fusion, are proposed to enhance the monitoring of industrial equipment. The work reported in this article is based on two components, a milling tool, and a motor, with sound, vibration, and ultrasound data collected in real experiments. Four main tasks were performed, namely the construction of the datasets, the training of the monitoring models without adding artificial noise to the data, the evaluation of the robustness of the previously trained models by injecting several levels of noise into the test data, and the optimization of the models by a proposed noisy training approach. The results show that the models maintain their performances at over 95\% accuracy despite adding noise in the test phase. These performances decrease by only 2\% at a considerable noise level of 15-dB signal-to-noise ratio (SNR). The noisy training method proved to be an optimal solution for improving the noise robustness and accuracy of convolutional deep learning models, whose performance regression of 2\% went from a noise level of 28 to 15 dB like the other models.}, language = {en} } @misc{AssafoLautschSuawaetal., author = {Assafo, Maryam and Lautsch, Martin and Suawa, Priscile Fogou and Jongmanns, Marcel and H{\"u}bner, Michael and Reichenbach, Marc and Brockmann, Carsten and Reinhardt, Denis and Langend{\"o}rfer, Peter}, title = {The ForTune Toolbox: Building Solutions for Condition-Based and Predictive Maintenance Focusing on Retrofitting}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-6204-0}, pages = {S. 541}, language = {en} }