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Documents authored by Wotawa, Franz

Artifact
Software
DOI: 10.4230/artifacts.22522
muehlburger/dx-2024-flex

Authors: Herbert Mühlburger and Franz Wotawa

Abstract
Cite as

Herbert Mühlburger, Franz Wotawa. muehlburger/dx-2024-flex (Software, Source Code and Data). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@misc{dagstuhl-artifact-22522,
   title = {{muehlburger/dx-2024-flex}}, 
   author = {M\"{u}hlburger, Herbert and Wotawa, Franz},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:e12a10f926844b17fac4162c73f2aa3a3d56b605;origin=https://github.com/muehlburger/dx-2024-flex;visit=swh:1:snp:326d582f384ea91b7969599deca2cbfece02c29f;anchor=swh:1:rev:c9e28964c6d49855e0741437b9d5e2435147b00c}{\texttt{swh:1:dir:e12a10f926844b17fac4162c73f2aa3a3d56b605}} (visited on 2024-11-28)},
   url = {https://github.com/muehlburger/dx-2024-flex},
   doi = {10.4230/artifacts.22522},
}
Document
Complete Volume
DOI: 10.4230/OASIcs.DX.2024
OASIcs, Volume 125, DX 2024, Complete Volume

Authors: Ingo Pill, Avraham Natan, and Franz Wotawa

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

Abstract
OASIcs, Volume 125, DX 2024, Complete Volume
Cite as

35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 1-534, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Proceedings{pill_et_al:OASIcs.DX.2024,
  title =	{{OASIcs, Volume 125, DX 2024, Complete Volume}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{1--534},
  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},
  URN =		{urn:nbn:de:0030-drops-222256},
  doi =		{10.4230/OASIcs.DX.2024},
  annote =	{Keywords: OASIcs, Volume 125, DX 2024, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.DX.2024.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Ingo Pill, Avraham Natan, and Franz Wotawa

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

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 0:i-0:xvi, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{pill_et_al:OASIcs.DX.2024.0,
  author =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{0:i--0:xvi},
  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.2024.0},
  URN =		{urn:nbn:de:0030-drops-222242},
  doi =		{10.4230/OASIcs.DX.2024.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/OASIcs.DX.2024.5
Bridging Hardware and Software Diagnosis: Leveraging Fault Signature Matrix and Spectrum-Based Fault Localization Similarities

Authors: Louise Travé-Massuyès and Franz Wotawa

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

Abstract
This paper examines two prominent Fault Detection and Isolation methodologies: the Signature Matrix approach, traditionally used in hardware systems, and the Spectrum-based approach, applied in software fault localization. Despite their distinct operational domains, both methods share the objective of precisely identifying and isolating faults. This study aims to compare these approaches and to highlight their similarities in principle. Through a comparative analysis, we assess how the structured pattern recognition of the Signature Matrix method and the statistical analysis capabilities of the Spectrum-based approach can be synergized to enhance diagnostic processes of cyber-physical systems that are composed of both hardware and software components. The investigation is motivated by the prospect of developing a hybrid Fault Detection and Isolation strategy that incorporates the robust detection mechanisms of hardware diagnostics with the techniques used in software fault localization. The findings are intended to advance the theoretical framework of Fault Detection and Isolation systems and suggest practical implementations across varied technological platforms, thereby improving the reliability and efficiency of fault detection and isolation in both hardware and software contexts.
Cite as

Louise Travé-Massuyès and Franz Wotawa. Bridging Hardware and Software Diagnosis: Leveraging Fault Signature Matrix and Spectrum-Based Fault Localization Similarities. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{travemassuyes_et_al:OASIcs.DX.2024.5,
  author =	{Trav\'{e}-Massuy\`{e}s, Louise and Wotawa, Franz},
  title =	{{Bridging Hardware and Software Diagnosis: Leveraging Fault Signature Matrix and Spectrum-Based Fault Localization Similarities}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{5:1--5:15},
  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.2024.5},
  URN =		{urn:nbn:de:0030-drops-220972},
  doi =		{10.4230/OASIcs.DX.2024.5},
  annote =	{Keywords: Diagnosis, Fault detection and identification, Software debugging}
}
Document
DOI: 10.4230/OASIcs.DX.2024.10
Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems

Authors: Lorenz Klampfl and Franz Wotawa

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

Abstract
Recent advancements in automated and autonomous driving systems have facilitated their integration into modern vehicles, enabling them to accurately perceive their surroundings and support or even fully undertake complex driving tasks. Given the complexity and unpredictable nature of driving environments and traffic situations, ensuring the correct behavior of such systems is essential to prevent hazardous situations, increase user acceptance, and avoid human harm. However, the increased complexity of these systems and the extensive search space of possible scenarios introduce significant challenges to testing and real-time fault management. Hence, besides rigorous testing during the development phase, there is a need for additional validation and verification during operation. This paper proposes utilizing Answer Set Programming (ASP), a form of declarative programming, for continuous real-time monitoring, fault detection, and explanation to ensure the correct functioning of automated and autonomous driving systems. Our approach aims to enhance the reliability and safety of such systems by detecting violations and providing explanations that can support fault-adaptive control or mitigation strategies. We demonstrate the effectiveness of our methodology across diverse scenarios executed within a simulation environment, discuss the main challenges encountered, and outline future research directions.
Cite as

Lorenz Klampfl and Franz Wotawa. Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{klampfl_et_al:OASIcs.DX.2024.10,
  author =	{Klampfl, Lorenz and Wotawa, Franz},
  title =	{{Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{10:1--10:20},
  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.2024.10},
  URN =		{urn:nbn:de:0030-drops-221023},
  doi =		{10.4230/OASIcs.DX.2024.10},
  annote =	{Keywords: Autonomous Driving, Answer Set Programming, Continuous Monitoring}
}
Document
DOI: 10.4230/OASIcs.DX.2024.18
Simulation-Based Diagnosis for Cyber-Physical Systems - A General Approach and Case Study on a Dual Three-Phase E-Machine

Authors: David Kaufmann, Matus Kozovsky, and Franz Wotawa

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

Abstract
This paper presents a simulation-based approach for fault diagnosis in cyber-physical systems. We utilize simulation models to generate training data for machine learning classifiers to detect faults and identify the root cause. The presented processing pipeline includes simulation model validation, training data generation, data preprocessing, and the implementation of a diagnosis method. A case study with a dual three-phase e-machine highlights the results and challenges of the simulation-based diagnosis approach. The e-machine simulation model provides a complex and robust system representation, including the capability to inject inter-turn short-circuit faults. The introduced validation procedures of the simulation model revealed limitations in signal similarity and distinguishability compared to real system behavior. Based on the discovered limitations, the overall best results are achieved by applying an Autoencoder model for anomaly detection, followed by a Random Forest classifier to identify the specific anomalies. Further, the focus is on identifying the affected e-machine phase rather than the exact number of faulty winding turns. The paper shows the challenges when applying a simulation-based diagnosis approach to time-series data and underlines the required analysis of simulation models. In addition, the flexible adaption in the diagnosis strategies enhances the efficient utilization of cyber-physical system models in fault diagnosis and root cause identification.
Cite as

David Kaufmann, Matus Kozovsky, and Franz Wotawa. Simulation-Based Diagnosis for Cyber-Physical Systems - A General Approach and Case Study on a Dual Three-Phase E-Machine. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 18:1-18:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{kaufmann_et_al:OASIcs.DX.2024.18,
  author =	{Kaufmann, David and Kozovsky, Matus and Wotawa, Franz},
  title =	{{Simulation-Based Diagnosis for Cyber-Physical Systems - A General Approach and Case Study on a Dual Three-Phase E-Machine}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{18:1--18:21},
  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.2024.18},
  URN =		{urn:nbn:de:0030-drops-221105},
  doi =		{10.4230/OASIcs.DX.2024.18},
  annote =	{Keywords: Cyber-Physical System, Fault diagnosis, Root cause analysis, Simulation-Based Diagnosis, Machine Learning, Artificial Neural Networks}
}
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.2024.22
Detecting Soft Faults in Heat Pumps (Short Paper)

Authors: Birgit Hofer and Franz Wotawa

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

Abstract
Heat pumps are critical for energy-efficient heating and cooling, but their performance can be compromised by soft faults like condenser silting. It is vital to detect and fix such faults early in order to ensure optimal performance and longevity of heat pump systems, and consequently optimize the positive effect of heat pumps to our environment. In this paper, we tackle the problem of early fault detection and propose a supervised machine learning approach that detects soft faults. In particular, we used a random forest approach to learn the regular behavior of heat pumps. We detect faults via comparing the expected behavior obtained from the learned model with the current behavior. In addition to the description of the used methodology, we provide and discuss the results obtained from an experimental study that is based on synthetic data of two different heat pumps.
Cite as

Birgit Hofer and Franz Wotawa. Detecting Soft Faults in Heat Pumps (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 22:1-22:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{hofer_et_al:OASIcs.DX.2024.22,
  author =	{Hofer, Birgit and Wotawa, Franz},
  title =	{{Detecting Soft Faults in Heat Pumps}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{22:1--22:10},
  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.2024.22},
  URN =		{urn:nbn:de:0030-drops-221145},
  doi =		{10.4230/OASIcs.DX.2024.22},
  annote =	{Keywords: Fault detection, heat pumps, supervised machine learning}
}
Document
Short Paper
DOI: 10.4230/OASIcs.DX.2024.24
Faster Diagnosis with Answer Set Programming (Short Paper)

Authors: Liliana Marie Prikler and Franz Wotawa

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

Abstract
From hardware to software to human patients, diagnosis has been one of the first areas of interest in artificial intelligence, and has remained a relevant topic since. Recent research in model-based diagnosis has shown that answer set programming not only allows for an easy expression of diagnosis problems, but also efficient solving. In this paper, we improve on previous results by making use of various modern answer set programming techniques. Our experiments compare multi-shot solving, heuristics and preferences, with results indicating that heuristics provide the fastest solutions on most instances we studied.
Cite as

Liliana Marie Prikler and Franz Wotawa. Faster Diagnosis with Answer Set Programming (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 24:1-24:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{prikler_et_al:OASIcs.DX.2024.24,
  author =	{Prikler, Liliana Marie and Wotawa, Franz},
  title =	{{Faster Diagnosis with Answer Set Programming}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{24:1--24:13},
  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.2024.24},
  URN =		{urn:nbn:de:0030-drops-221160},
  doi =		{10.4230/OASIcs.DX.2024.24},
  annote =	{Keywords: Answer set programming, model-based diagnosis, performance comparison}
}
Document
Short Paper
DOI: 10.4230/OASIcs.DX.2024.25
FLEX: Fault Localization and Explanation Using Open-Source Large Language Models in Powertrain Systems (Short Paper)

Authors: Herbert Muehlburger and Franz Wotawa

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

Abstract
Cyber-physical systems (CPS) are critical to modern infrastructure, but are vulnerable to faults and anomalies that threaten their operational safety. In this work, we evaluate the use of open-source Large Language Models (LLMs), such as Mistral 7B, Llama3.1:8b-instruct-fp16, and others to detect anomalies in two distinct datasets: battery management and powertrain systems. Our methodology utilises retrieval-augmented generation (RAG) techniques, incorporating a novel two-step process where LLMs first infer operational rules from normal behavior before applying these rules for fault detection. During the experiments, we found that the original prompt design yielded strong results for the battery dataset but required modification for the powertrain dataset to improve performance. The adjusted prompt, which emphasises rule inference, significantly improved anomaly detection for the powertrain dataset. Experimental results show that models like Mistral 7B achieved F1-scores up to 0.99, while Llama3.1:8b-instruct-fp16 and Gemma 2 reached perfect F1-scores of 1.0 in complex scenarios. These findings demonstrate the impact of effective prompt design and rule inference in improving LLM-based fault detection for CPS, contributing to increased operational resilience.
Cite as

Herbert Muehlburger and Franz Wotawa. FLEX: Fault Localization and Explanation Using Open-Source Large Language Models in Powertrain Systems (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 25:1-25:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{muehlburger_et_al:OASIcs.DX.2024.25,
  author =	{Muehlburger, Herbert and Wotawa, Franz},
  title =	{{FLEX: Fault Localization and Explanation Using Open-Source Large Language Models in Powertrain Systems}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{25:1--25:14},
  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.2024.25},
  URN =		{urn:nbn:de:0030-drops-221170},
  doi =		{10.4230/OASIcs.DX.2024.25},
  annote =	{Keywords: Fault detection, anomaly detection, powertrain systems, large language models, open-source LLMs}
}
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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