| Online feature learning for condition monitoring of rotating machinery S Martin-del-Campo, F Sandin Engineering Applications of Artificial Intelligence 64, 187-196, 2017 | 50 | 2017 |
| Phobos 2/ASPERA data revisited: Planetary ion escape rate from Mars near the 1989 solar maximum R Ramstad, Y Futaana, S Barabash, H Nilsson, S Martin del Campo B, ... Geophysical Research Letters 40 (3), 477-481, 2013 | 37 | 2013 |
| Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learning S Martin-del-Campo, S Schnabel, F Sandin, P Marklund Tribology International 132, 30-38, 2019 | 24 | 2019 |
| FPGA prototype of machine learning analog-to-feature converter for event-based succinct representation of signals SM del Campo, K Albertsson, J Nilsson, J Eliasson, F Sandin 2013 IEEE International Workshop on Machine Learning for Signal Processing …, 2013 | 23 | 2013 |
| Dictionary learning approach to monitoring of wind turbine drivetrain bearings S Martin-del-Campo, F Sandin, D Strömbergsson International Journal of Computational Intelligence Systems 14 (1), 106-121, 2020 | 21 | 2020 |
| Towards zero-configuration condition monitoring based on dictionary learning S Martin-del-Campo, F Sandin 2015 23rd European Signal Processing Conference (EUSIPCO), 1306-1310, 2015 | 14 | 2015 |
| Unsupervised feature learning applied to condition monitoring S Martín del Campo Barraza Luleå University of Technology, 2017 | 9 | 2017 |
| Addressing uncertainties within active learning for industrial IoT D Agarwal, P Srivastava, S Martin-del-Campo, B Natarajan, B Srinivasan 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 557-562, 2021 | 8 | 2021 |
| Dictionary learning with equiprobable matching pursuit F Sandin, S Martin-del-Campo 2017 International Joint Conference on Neural Networks (IJCNN), 557-564, 2017 | 8 | 2017 |
| Cloud-based collaborative learning (ccl) for the automated condition monitoring of wind farms S Javed, S Javed, J van Deventer, F Sandin, J Delsing, M Liwicki, ... 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems …, 2022 | 6 | 2022 |
| Kinematic frequencies of rotating equipment identified with sparse coding and dictionary learning S Martin-del-Campo, F Sandin, S Schnabel Proceedings of the annual conference of the PHM society (ed Scott Clements …, 2019 | 5 | 2019 |
| Exploratory analysis of acoustic emissions in steel using dictionary learning S Martin-del-Campo, F Sandin, S Schnabel, P Marklund, J Delsing 2016 IEEE International Ultrasonics Symposium (IUS), 1-4, 2016 | 5 | 2016 |
| Unsupervised ranking of outliers in wind turbines via isolation forest with dictionary learning S Martin-del-Campo, K Al-Kahwati 5th European Conference of the Prognostics and Health Management (PHM …, 2020 | 3 | 2020 |
| Active learning framework for time-series classification of vibration and industrial process data SM del Campo Barraza, W Lindskog, D Badalotti, O Liew, A Toyser Annual Conference of the PHM Society 13 (1), 2021 | 2 | 2021 |
| Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning S Martin-del-Campo, F Sandin, S Schnabel Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2021 | 2 | 2021 |
| Dataset concerning the vibration signals from wind turbines in northern Sweden S Martin del Campo Barraza, F Sandin, D Strömbergsson | 1 | 2018 |
| Towards autonomous condition monitoring sensor systems S Martín del Campo Barraza Luleå tekniska universitet, 2015 | | 2015 |
Fredrik SandinProfessor in Machine Learning, Luleå University of Technology, EISLAB, SwedenVerified email at ltu.se
