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Documents authored by Muñoz Gutiérrez, Stan

Document
DOI: 10.4230/OASIcs.DX.2025.9
Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis

Authors: Stan Muñoz Gutiérrez and Franz Wotawa

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
This paper introduces Spectral Fault Receptive Fields (SFRFs), a biologically inspired technique for degradation state assessment in bearing fault diagnosis and remaining useful life (RUL) estimation. Drawing on the center-surround organization of retinal ganglion cell receptive fields, we propose a frequency-domain feature extraction algorithm that enhances the detection of fault signatures in vibration signals. SFRFs are designed as antagonistic spectral filters centered on characteristic fault frequencies, with inhibitory surrounds that enable robust characterization of incipient faults under variable operating conditions. A multi-objective evolutionary optimization strategy based on NSGA-II algorithm is employed to tune the receptive field parameters by simultaneously minimizing RUL prediction error, maximizing feature monotonicity, and promoting smooth degradation trajectories. The method is demonstrated on the XJTU-SY bearing run-to-failure dataset, confirming its suitability for constructing condition indicators in health monitoring applications. Key contributions include: (i) the introduction of SFRFs, inspired by the biology of vision in the primate retina; (ii) an evolutionary optimization framework guided by condition monitoring and prognosis criteria; and (iii) experimental evidence supporting the detection of early-stage faults and their precursors. Furthermore, we confirm that our diagnosis-informed spectral representation achieves accurate RUL prediction using a bagging regressor. The results highlight the interpretability and principled design of SFRFs, bridging signal processing, biological sensing principles, and data-driven prognostics in rotating machinery.
Cite as

Stan Muñoz Gutiérrez and Franz Wotawa. Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 9:1-9:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{munozgutierrez_et_al:OASIcs.DX.2025.9,
  author =	{Mu\~{n}oz Guti\'{e}rrez, Stan and Wotawa, Franz},
  title =	{{Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{9:1--9:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.9},
  URN =		{urn:nbn:de:0030-drops-247986},
  doi =		{10.4230/OASIcs.DX.2025.9},
  annote =	{Keywords: Health Perception, Spectral Fault Receptive Fields, Remaining Useful Life, Incipient Fault Diagnosis, Prognostics and Health Management, Condition Monitoring, Evolutionary Multi-Objective Optimization, Bagged Regression Tree Ensemble, Bearing Fault Diagnosis}
}
Document
Short Paper
DOI: 10.4230/OASIcs.DX.2024.20
Data-Driven Diagnosis of Electrified Vehicles: Results from a Structured Literature Review (Short Paper)

Authors: Stan Muñoz Gutiérrez, Adil Mukhtar, and Franz Wotawa

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
Traditional onboard vehicle diagnostics are rapidly evolving concomitant to the rise of electrified powertrains, digital transformation, and intelligent technologies for advanced system management. The big data now available in modern vehicles offers unprecedented opportunities for condition monitoring and prognosis, but also presents challenges in scaling and integrating multimodal sensor data across components with varying timescale dynamics. Machine learning techniques have proven particularly effective in implementing diagnostic functions within electrified vehicle powertrains. This study systematically reviews intelligent, data-driven techniques for health monitoring and prognosis of electrified powertrains. We categorize existing research based on diagnostic functions and machine learning methods, with a focus on approaches that do not require prior knowledge of faulty operational states. Our findings indicate that deep learning methods are state-of-the-art across several diagnostic functions, fault modes, system levels, and multimodal sensor integration.
Cite as

Stan Muñoz Gutiérrez, Adil Mukhtar, and Franz Wotawa. Data-Driven Diagnosis of Electrified Vehicles: Results from a Structured Literature Review (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 20:1-20:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{munozgutierrez_et_al:OASIcs.DX.2024.20,
  author =	{Mu\~{n}oz Guti\'{e}rrez, Stan and Mukhtar, Adil and Wotawa, Franz},
  title =	{{Data-Driven Diagnosis of Electrified Vehicles: Results from a Structured Literature Review}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{20:1--20:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.20},
  URN =		{urn:nbn:de:0030-drops-221128},
  doi =		{10.4230/OASIcs.DX.2024.20},
  annote =	{Keywords: Diagnostic functions, Machine Learning, Powertrain, Electrified vehicles}
}
Document
Short Paper
DOI: 10.4230/OASIcs.DX.2024.29
Transformer-Based Signal Inference for Electrified Vehicle Powertrains (Short Paper)

Authors: Stan Muñoz Gutiérrez, Adil Mukhtar, and Franz Wotawa

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
The scarcity of labeled data for intelligent diagnosis of non-linear technical systems is a common problem for developing robust and reliable real-world applications. Several deep learning approaches have been developed to address this challenge, including self-supervised learning, representation learning, and transfer learning. Due largely to their powerful attention mechanisms, transformers excel at capturing long-term dependencies across multichannel and multi-modal signals in sequential data, making them suitable candidates for time series modeling. Despite their potential, studies applying transformers for diagnostic functions, especially in signal reconstruction through representation learning, remain limited. This paper aims to narrow this gap by identifying the requirements and potential of transformer self-attention mechanisms for developing auto-associative inference engines that learn exclusively from healthy behavioral data. We apply a transformer backbone for signal reconstruction using simulated data from a simplified powertrain. Feedback from these experiments, and the reviewed evidence from the literature, allows us to conclude that autoencoder and autoregressive approaches are potentiated by transformers.
Cite as

Stan Muñoz Gutiérrez, Adil Mukhtar, and Franz Wotawa. Transformer-Based Signal Inference for Electrified Vehicle Powertrains (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 29:1-29:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{munozgutierrez_et_al:OASIcs.DX.2024.29,
  author =	{Mu\~{n}oz Guti\'{e}rrez, Stan and Mukhtar, Adil and Wotawa, Franz},
  title =	{{Transformer-Based Signal Inference for Electrified Vehicle Powertrains}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{29:1--29:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.29},
  URN =		{urn:nbn:de:0030-drops-221217},
  doi =		{10.4230/OASIcs.DX.2024.29},
  annote =	{Keywords: Signal Inference, Deep Learning, Self-Supervised Learning, Multimodal Transformer Autoencoder, Electric Vehicle, Powertrain, Electric Motor}
}
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