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Arman Mohammadi

PhD student

Publication list

Journal papers

Arman Mohammadi, Mattias Krysander, Daniel Jung,  Consistency-based diagnosis using data-driven residuals and limited training data, Control Engineering Practice 159:106283 (2025)  https://doi.org/10.1016/j.conengprac.2025.106283

Conference papers

Niklas Allansson, Arman Mohammadi, Daniel Jung, Mattias Krysander,  Fuel injection fault diagnosis using structural analysis and data-driven residuals, IFAC PAPERSONLINE, pp. 360-365, ELSEVIER (2024)  https://doi.org/10.1016/j.ifacol.2024.07.244

Theodor Westny, Arman Mohammadi, Daniel Jung, Erik Frisk,  Stability-Informed Initialization of Neural Ordinary Differential Equations, Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024, Neil Lawrence (ed.), Proceedings of Machine Learning Research, pp. 52903-52914, PMLR (2024)

Arman Mohammadi, Theodor Westny, Daniel Jung, Mattias Krysander,  Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems, IFAC PAPERSONLINE, pp. 2909-2914, ELSEVIER (2023)  https://doi.org/10.1016/j.ifacol.2023.10.1411

Daniel Jung, Mattias Krysander, Arman Mohammadi,  Fault diagnosis using data-driven residuals for anomaly classification with incomplete training data, IFAC PAPERSONLINE, pp. 2903-2908, ELSEVIER (2023)  https://doi.org/10.1016/j.ifacol.2023.10.1410

Arman Mohammadi, Mattias Krysander, Daniel Jung,  Analysis of grey-box neural network-based residuals for consistency-based fault diagnosis, , IFAC papers online, pp. 1-6, Elsevier (2022)  https://doi.org/10.1016/j.ifacol.2022.07.097

Preprints

Arman Mohammadi, Theodor Westny, Daniel Jung, Mattias Krysander,  Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems (2023)  https://doi.org/10.48550/arXiv.2305.04670  https://arxiv.org/abs/2305.04670

PhD theses

Arman Mohammadi,  Machine Learning for Fault Diagnosis of Industrial Systems, Linköping Studies in Science and Technology. Dissertations 2497, Linköping University Electronic Press, Linköping (2025)  https://doi.org/10.3384/9789181183924