mlsanityData to 7 Oct 2026

A Hybrid Probabilistic Battery Health Management Approach for Robust Inspection Drone Operations

arXiv 2405.00055 ↗First posted 24 Apr 2024eess.SY cs.AI cs.LGcited by 2

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Abstract (arXiv)

Health monitoring of remote critical infrastructure is a complex and expensive activity due to the limited infrastructure accessibility. Inspection drones are ubiquitous assets that enhance the reliability of critical infrastructures through improved accessibility. However, due to the harsh operation environment, it is crucial to monitor their health to ensure successful inspection operations. The battery is a key component that determines the overall reliability of the inspection drones and, with an appropriate health management approach, contributes to reliable and robust inspections. In this context, this paper presents a novel hybrid probabilistic approach for battery end-of-discharge (EOD) voltage prediction of Li-Po batteries. The hybridization is achieved in an error-correction configuration, which combines physics-based discharge and probabilistic error-correction models to quantify the aleatoric and epistemic uncertainty. The performance of the hybrid probabilistic methodology was empirically evaluated on a dataset comprising EOD voltage under varying load conditions. The dataset was obtained from real inspection drones operated on different flights, focused on offshore wind turbine inspections. The proposed approach has been tested with different probabilistic methods and demonstrates 14.8% improved performance in probabilistic accuracy compared to the best probabilistic method. In addition, aleatoric and epistemic uncertainties provide robust estimations to enhance the diagnosis of battery health-states.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 78 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

14 of 78 references.

References · 14

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  4. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  5. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 813
  6. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 417
  7. 2015Convolutional neural networks with low-rank regularizationcited by 102
  8. 2020Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantificationcited by 77
  9. 2020Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applicationscited by 63
  10. 2021Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantificationcited by 57
  11. 2020Fusing Physics-based and Deep Learning Models for Prognosticscited by 39
  12. 2020A Physics-Guided Neural Network Framework for Elastic Plates: Comparison of Governing Equations-Based and Energy-Based Approachescited by 34
2 more
  1. 2018Uncertainty-Aware Attention for Reliable Interpretation and Predictioncited by 24
  2. 2023B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterizationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Bayesian Physics Informed Neural Networks for Reliable Transformer Prognosticscited by 3
  2. 2026Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognosticscited by 0