TY - GEN A1 - Suawa Fogou, Priscile A1 - Halbinger, Anja A1 - Jongmanns, Marcel A1 - Reichenbach, Marc T1 - Noise-Robust Machine Learning Models for Predictive Maintenance Applications T2 - IEEE Sensors Journal N2 - 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. KW - Accelerometer Y1 - 2023 UR - https://ieeexplore.ieee.org/document/10122864 U6 - https://doi.org/10.1109/JSEN.2023.3273458 SN - 1558-1748 SN - 1530-437X VL - 23 IS - 13 SP - 15081 EP - 15092 ER - TY - GEN A1 - Hoffmann, Javier Eduardo A1 - Mahmood, Safdar A1 - Suawa Fogou, Priscile A1 - George, Nevin A1 - Raha, Solaiman A1 - Safi, Sabur A1 - Schmailzl, Kurt JG A1 - Brandalero, Marcelo A1 - Hübner, Michael T1 - A Survey on Machine Learning Approaches to ECG Processing T2 - 2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 23-25 Sept. 2020 , Poznan, Poland Y1 - 2020 UR - https://ieeexplore.ieee.org/document/9241283 SN - 978-83-62065-39-4 SN - 978-83-62065-37-0 SN - 978-1-7281-7746-5 ER - TY - GEN A1 - Assafo, Maryam A1 - Lautsch, Martin A1 - Suawa, Priscile Fogou A1 - Jongmanns, Marcel A1 - Hübner, Michael A1 - Reichenbach, Marc A1 - Brockmann, Carsten A1 - Reinhardt, Denis A1 - Langendörfer, Peter T1 - The ForTune Toolbox: Building Solutions for Condition-Based and Predictive Maintenance Focusing on Retrofitting Y1 - 2023 SN - 978-3-8007-6204-0 SN - 978-3-8007-6203-3 N1 - Poster, Tagungsband MikroSystemTechnik Kongress 2023, Dresden, 23. - 25. Oktober 2023 SP - S. 541 PB - VDE Verlag CY - Berlin ER - TY - GEN A1 - Suawa Fogou, Priscile A1 - Meisel, Tenia A1 - Jongmanns, Marcel A1 - Hübner, Michael A1 - Reichenbach, Marc T1 - Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion T2 - Sensors Y1 - 2022 U6 - https://doi.org/10.3390/s22093516 SN - 1424-8220 VL - 22 IS - 9 ER -