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Documents authored by Diaz-Gonzalez, Abel

Document
DOI: 10.4230/OASIcs.DX.2025.11
A Data-Driven Particle Filter Approach for System-Level Prediction of Remaining Useful Life

Authors: Abel Diaz-Gonzalez, Austin Coursey, Marcos Quinones-Grueiro, and Gautam Biswas

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

Abstract
Accurate estimation of the remaining useful life (RUL) of industrial systems is a critical component of predictive maintenance strategies. This work presents a data-driven method for RUL prediction that also quantifies uncertainty, drawing inspiration from model-based particle filtering techniques. Instead of simulating system state transitions, we model degradation as a stochastic process governed by performance metrics and use a Bayesian particle filtering framework to infer its underlying parameters. Our approach bypasses traditional state-space modeling by directly estimating the end-of-life distribution from observed performance data. Key characteristics of the filter, such as propagation noise and observation correction strength, are adapted over time based on current observations and past predictive performance, enabling better capture of future uncertainty. We evaluate the proposed method using an unmanned aerial vehicle simulation dataset developed for system-level prognostics research, which includes high-fidelity degradation signals and ground-truth system performance metrics for validating predictive accuracy.
Cite as

Abel Diaz-Gonzalez, Austin Coursey, Marcos Quinones-Grueiro, and Gautam Biswas. A Data-Driven Particle Filter Approach for System-Level Prediction of Remaining Useful Life. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 11:1-11:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{diazgonzalez_et_al:OASIcs.DX.2025.11,
  author =	{Diaz-Gonzalez, Abel and Coursey, Austin and Quinones-Grueiro, Marcos and Biswas, Gautam},
  title =	{{A Data-Driven Particle Filter Approach for System-Level Prediction of Remaining Useful Life}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{11:1--11:13},
  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.2025.11},
  URN =		{urn:nbn:de:0030-drops-248006},
  doi =		{10.4230/OASIcs.DX.2025.11},
  annote =	{Keywords: remaining useful life, particle filter methods, data-driven methods, system-level prognostics, performance metrics}
}
Document
DX Competition
DOI: 10.4230/OASIcs.DX.2025.15
Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships (DX Competition)

Authors: Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro, and Gautam Biswas

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

Abstract
Fault detection and isolation are becoming increasingly important as modern systems become more complex. To encourage the development of new fault detection solutions that can operate with limited noisy data and an incomplete mathematical model, the DX 2025 LiU-ICE competition for diagnosis of the air path of an internal combustion engine was introduced. In this paper, we present our winning solution to this competition. Our fault detection architecture starts with a semi-supervised Transformer Autoencoder trained to reconstruct nominal data. Detected faults are then passed through a rule-based fault persistence filter that aims to suppress false positives. Once a fault is detected, we use four neural networks trained to estimate features determined from structural analysis of a partial system model. The residuals of these networks are fed to a supervised fault classification network that estimates the fault probabilities. With this architecture, we achieved an 87% detection rate with a 0% false alarm rate on the provided competition data. Additionally, our isolation architecture assigned the correct fault 73.8% probabilty on average. On unseen competition data from a new driving cycle, we achieved a 100% detection rate and assigned the correct fault 66.2% probability on average. On the other hand, the Transformer Autoencoder failed to transfer to the new driving conditions, causing many false alarms. We discuss ways future work can reduce this.
Cite as

Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro, and Gautam Biswas. Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships (DX Competition). In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 15:1-15:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{coursey_et_al:OASIcs.DX.2025.15,
  author =	{Coursey, Austin and Diaz-Gonzalez, Abel and Quinones-Grueiro, Marcos and Biswas, Gautam},
  title =	{{Data-Driven Fault Detection and Isolation Enhanced with System Structural Relationships}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{15:1--15:17},
  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.2025.15},
  URN =		{urn:nbn:de:0030-drops-248043},
  doi =		{10.4230/OASIcs.DX.2025.15},
  annote =	{Keywords: fault detection, fault isolation, autoencoder}
}
Document
Short Paper
DOI: 10.4230/OASIcs.DX.2024.21
Data-Driven RUL Prediction Using Performance Metrics (Short Paper)

Authors: Abel Diaz-Gonzalez, Austin Coursey, Marcos Quinones-Grueiro, Chetan S. Kulkarni, and Gautam Biswas

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

Abstract
Prognostics is the scientific study of component and system degradation with use, and the prediction of when failure may occur. In this work, we propose a new data-driven method for predicting a system’s remaining useful life (RUL) without needing an accurate system model or expert knowledge. Instead, we use system operational data to estimate how the system’s performance metrics change with time. Although this is a purely data-driven approach, the method’s design is inspired by model-based techniques. First, we frame a novel Multitask Machine Learning architecture to simultaneously learn the general pattern of performance degradation and the individual trajectories from run-to-failure performance trajectory data. We apply this method to the set of performance metrics that determine the system’s end-of-life (EOL), building a performance trajectory library of the system operation under different operational conditions. We leverage the performance metric library as prior belief and develop a Bayesian deep learning approach to update the performance measures over time and predict the system EOL. We evaluate our method on two datasets of the N-CMAPSS benchmark, achieving satisfactory results in terms of overall performance and uncertainty estimation accuracy. Overall, our approach illustrates a generalized deep learning architecture that can more effectively predict the system RUL for a collection of identical systems.
Cite as

Abel Diaz-Gonzalez, Austin Coursey, Marcos Quinones-Grueiro, Chetan S. Kulkarni, and Gautam Biswas. Data-Driven RUL Prediction Using Performance Metrics (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 21:1-21:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{diazgonzalez_et_al:OASIcs.DX.2024.21,
  author =	{Diaz-Gonzalez, Abel and Coursey, Austin and Quinones-Grueiro, Marcos and Kulkarni, Chetan S. and Biswas, Gautam},
  title =	{{Data-Driven RUL Prediction Using Performance Metrics}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{21:1--21:15},
  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.2024.21},
  URN =		{urn:nbn:de:0030-drops-221135},
  doi =		{10.4230/OASIcs.DX.2024.21},
  annote =	{Keywords: remaining useful life, data-driven methods, machine learning, performance metric, multitask machine learning, Monte Carlo}
}
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