Search Results

Documents authored by Provan, Gregory

Document
Complete Volume
DOI: 10.4230/OASIcs.DX.2026
OASIcs, Volume 148, DX 2026, Complete Volume

Authors: Ingo Pill, Marina Zanella, and Gregory Provan

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract
OASIcs, Volume 148, DX 2026, Complete Volume
Cite as

37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 1-342, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@Proceedings{pill_et_al:OASIcs.DX.2026,
  title =	{{OASIcs, Volume 148, DX 2026, Complete Volume}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{1--342},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026},
  URN =		{urn:nbn:de:0030-drops-280802},
  doi =		{10.4230/OASIcs.DX.2026},
  annote =	{Keywords: OASIcs, Volume 148, DX 2026, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.DX.2026.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Ingo Pill, Marina Zanella, and Gregory Provan

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

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

37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{pill_et_al:OASIcs.DX.2026.0,
  author =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{0:i--0:xii},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.0},
  URN =		{urn:nbn:de:0030-drops-280799},
  doi =		{10.4230/OASIcs.DX.2026.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/OASIcs.DX.2026.1
A Realization-Theoretic Foundation for Fault Diagnosis, with Diagnosability Equivalence Predictions

Authors: Gregory Provan

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract
Fault diagnosis encompasses a wide range of paradigms, including observer-based fault detection and isolation, consistency-based diagnosis, Bayesian diagnosis, geometric diagnosis, and structured machine-learning approaches. Despite their shared objective, these paradigms are formulated in different mathematical languages and are rarely analyzed within a common framework. This paper introduces a realization-theoretic foundation for diagnosis based on the Diagnostic System Specification (DSS), a representation that separates behavioural models, realizations, measurement fields, and inference procedures. We define diagnostic invariants as symmetry-preserving quantities of realized system structures and show that detectability is fundamentally an orbit separation property in the realization’s symmetry space. Building on this observation, we develop a theory of realizations in which diagnostic models appear as objects equipped with invariant families and nominal symmetry groups. We show that diagnostic paradigms differ not in their underlying architecture but in the realizations they induce and the invariants they expose. Our main result addresses the question: is there a single mathematical condition of which diagnosis approaches are equivalent in terms of diagnosability? We show that using orbit separation on a paradigm-appropriate realization shows the following: faults are distinguishable iff they lie in different orbits of the realization’s nominal symmetry group. We accomplish this by constructing a minimal sufficient realization associated with a measurement field and characterize it by a universal property. This yields a structural notion of diagnostic equivalence and provides a principled basis for comparing methods across traditionally separate paradigms.
Cite as

Gregory Provan. A Realization-Theoretic Foundation for Fault Diagnosis, with Diagnosability Equivalence Predictions. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{provan:OASIcs.DX.2026.1,
  author =	{Provan, Gregory},
  title =	{{A Realization-Theoretic Foundation for Fault Diagnosis, with Diagnosability Equivalence Predictions}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{1:1--1:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.1},
  URN =		{urn:nbn:de:0030-drops-278156},
  doi =		{10.4230/OASIcs.DX.2026.1},
  annote =	{Keywords: Model-based Diagnosis, Diagnosability, System Realization Theory}
}
Document
DOI: 10.4230/OASIcs.DX.2026.13
Statistical Foundations of Local Geometric Fault Detection: The Curvature Channel

Authors: Gregory Provan

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract
Geometric and topological methods have recently emerged as a promising approach to fault detection in hybrid and cyber-physical systems. Unlike classical residual-based methods, which detect violations of a parametric model, geometric methods monitor changes in the local structure of trajectories through quantities such as intrinsic dimension, smoothness, tangent-cone geometry, and consistency with physical constraints. Despite encouraging empirical results, there is currently no theory characterising when such geometric signatures are detectable, distinguishable, or reliable under noise. This paper develops a statistical theory of local geometric fault detection, with a focus on curvature-based approaches. We study faults detectable by displacement in a curvature signature space, and derive the noise floor of geometric estimators based on finite differences and local neighbourhood averaging. The resulting estimator variance scales as σ_{est} = O(σ/(Δ t²√k)), exposing the fundamental trade-off between sampling rate, noise level, and neighbourhood size. Using this noise model, we derive geometric analogues of the classical detectability and isolability conditions of fault-detection theory, expressed directly in terms of separation in signature space. We further prove an exclusion result showing that a broad class of smooth parametric faults cannot activate curvature-based geometric channels, yielding a form of label-free fault-class identification. These results lead to a practical pre-deployment assessment procedure that determines, from nominal data alone, whether a curvature-based fault detector is expected to achieve a specified false-alarm rate, detection probability, and isolation capability. The theory thereby places local geometric fault detection on the same statistical footing as classical residual-based diagnosis while clarifying its distinctive strengths, limitations, and operating regime.
Cite as

Gregory Provan. Statistical Foundations of Local Geometric Fault Detection: The Curvature Channel. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 13:1-13:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{provan:OASIcs.DX.2026.13,
  author =	{Provan, Gregory},
  title =	{{Statistical Foundations of Local Geometric Fault Detection: The Curvature Channel}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{13:1--13:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.13},
  URN =		{urn:nbn:de:0030-drops-278279},
  doi =		{10.4230/OASIcs.DX.2026.13},
  annote =	{Keywords: Geometric metrics, Diagnosis methods, Theory}
}
Document
DOI: 10.4230/OASIcs.DX.2025.10
Automating Control System Design: Using Language Models for Expert Knowledge in Decentralized Controller Auto-Tuning

Authors: Marlon J. Ares-Milian, Gregory Provan, and Marcos Quinones-Grueiro

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

Abstract
Fully-automated optimal controller design for engineering systems is a challenging task. While, optimization-based, automated control parameter tuning techniques have been widely discussed in the literature, most works do not discuss expert knowledge requirements for system design, which result in significant human intervention. In this work, we discuss a multistage controller tuning framework for decentralized control that highlights expert knowledge requirements in automated controller design. We propose a methodology to automate the input-output pairing and stage definition steps in the framework using Large Language Models (LLMs) for a family of multi-tank benchmarks. We achieve this by proposing a mathematical language to describe the system and design an algorithm to bind this mathematical representation to the input prompt space of an LLM. We demonstrate that our methodology can produce consistent expert knowledge outputs from the LLM with over 97% accuracy for the multi-tank benchmarks. We also empirically show that, correct stage definition by the LLM can improve tuned controller performance by up to 52%.
Cite as

Marlon J. Ares-Milian, Gregory Provan, and Marcos Quinones-Grueiro. Automating Control System Design: Using Language Models for Expert Knowledge in Decentralized Controller Auto-Tuning. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{aresmilian_et_al:OASIcs.DX.2025.10,
  author =	{Ares-Milian, Marlon J. and Provan, Gregory and Quinones-Grueiro, Marcos},
  title =	{{Automating Control System Design: Using Language Models for Expert Knowledge in Decentralized Controller Auto-Tuning}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{10:1--10:20},
  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.2025.10},
  URN =		{urn:nbn:de:0030-drops-247996},
  doi =		{10.4230/OASIcs.DX.2025.10},
  annote =	{Keywords: controller auto-tuning, automated system design, large language models}
}

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact