Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 12

Sep 12, 2026cs.AI

AI-Powered Flare Combustion Efficiency Estimation

Achieving high combustion efficiency in flare stacks is crucial for adhering to regulatory standards and controlling the release of hydrocarbons into the environment. Traditional instruments like gas analyzers and hyperspectral cameras are expensive, fragile, and require frequent calibration, which makes them impractical for remote or budget constrained industrial sites. We propose an innovative solution that combines a lightweight vision-language encoder with a compact multi-layer perceptron to predict combustion efficiency directly from low-cost thermal video footage. The fully trained model is integrated into an easy-to-deploy graphical user interface. This interface overlays predicted combustion efficiency values on each video frame, displays real-time trends in combustion efficiency, shows the distribution of combustion efficiency across all frames in the video, and allows users to export CSV reports. Over a six-month period, the system achieved 99% uptime and required less than 15 minutes of maintenance per week.

Afeefa Azam, Iyyakutti Iyappan Ganapathi, Fares Ossama Abdelhafez +5

Computer Science Department, Khalifa University of Science and Technology, Abu Dhabi, UAE · Mechanical & Nuclear Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE · Digital Projects & Innovation Division, ADNOC, Abu Dhabi, UAE

Combustion Control Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Wildfire Detection Efficiency

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Aug 5, 2026cs.LG

MGSB: Manifold Gated Signature Branch Pressure-Domain Baseline Architecture for Two-Phase Pipeline Flows Under Distributional Shift

Leak detection models for multiphase pipelines often degrade when deployed under flow regimes that differ from training. Existing evaluations typically assess performance under in-distribution operating conditions, masking failures caused by regime transitions such as bubble-to-slug flow. We propose the Manifold Gated Signature Bias (MGSB), a regime-aware architecture combining regime-conditioned feature fusion, a TT-RoughPath encoder, and Mean-Teacher consistency regularization to improve robustness under distribution shift. Under leave-one-group-out evaluation, MGSB achieves a detection F1 of 0.930 and an OOD F1 of 0.783, substantially outperforming CNN-LSTM and fully connected baselines under severe feature corruption. Ablations show the proposed architecture, not the training procedure, is the primary contributor to OOD robustness, while Mahalanobis-distance analysis confirms the held-out conditions are genuinely out-of-distribution. These results show that explicit regime-aware modelling is a practical path toward robust, sensor-agnostic leak detection in industrial multiphase pipelines.

Issah Suleiman, Sormeh Serpoosh, Nadine Elkholy +3

Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Out-Of-Distribution Detection Leakage Unsupervised Detection Distributional Signatures Distribution Shifts+1

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Jul 26, 2026cs.LG

An adaptive multi-fuzzy logic model for diagnosing transformer faults using dynamic weight optimization

Dissolved gas analysis (DGA) is crucial for diagnosing early power transformer failures. Traditional DGA interpretation methods like Duval Triangle, IEC ratio, Roger ratio, Doernenburg ratio and Key Gas are inconsistent and vary in accuracy, especially for multiple fault conditions. We propose an Adaptive Multi-Fuzzy Logic (AMFL) model integrating multiple DGA methods with fuzzy logic and a dynamic weight adjustment mechanism. Unlike existing approaches with fixed weights, this system iteratively evaluates each method's diagnostic performance, identifies multiple fault types, and adjusts weights based on fault prediction accuracy. A feedback-based optimization recalibrates weights after each cycle to ensure optimal solution convergence. The model, implemented in MATLAB/Simulink, is validated against DGA datasets with known error conditions. Results show the AMFL model significantly improves diagnostic accuracy, especially in complex error scenarios, and enhances adaptability to new datasets. Comparative analysis demonstrates the proposed method outperforms traditional fixed weight multi-fuzzy systems in accuracy, consistency, and reliability of error detection. This work provides a robust, flexible diagnostic tool for transformer condition monitoring and supports more accurate asset management decisions.

Kim-Anh Nguyen, Huy Hoang Le, Ba Tu Phung

Faculty of Electrical Engineering, the University of Danang - University of Science and Technology, Da Nang, Vietnam

Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Robot Failures Fuzzy Transformer Architectures

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Jul 20, 2026cs.NE

Optimizing Sensor Placement for Hydrogen Leak Detection in Enclosed Infrastructure: A Comparative Study Using CFD-informed Genetic Algorithm and DeepSets Neural Surrogate

Hydrogen infrastructure in enclosed environments, such as parking facilities for fuel cell vehicles, presents significant safety challenges due to hydrogen's low ignition energy and wide flammability range. Current monitoring systems are largely reactive, detecting leaks only after hazardous concentrations have formed. This study develops a computational framework for proactive sensor placement optimization by integrating computational fluid dynamics (CFD), genetic algorithm (GA) optimization, and a DeepSets neural surrogate. A CFD database of 180 scenarios was generated for a representative 50 m x 30 m x 3 m garage, covering multiple leak positions, rates (1-150 g/s), and ventilation conditions (ACH = 3-10 per hour). Sensor placement was optimized using a multi-objective GA and compared with uniform, random, and surrogate-assisted approaches. The GA achieved a detection rate of 96.1% within 60 s and reduced blind areas to 0.12%, corresponding to an approximately 5% improvement in composite fitness over a uniform baseline. The DeepSets surrogate reproduced near-optimal configurations with a fitness gap below 0.01 while reducing CFD evaluations by 89% and computational time by two orders of magnitude. Detection performance and spatial coverage remained comparable to the GA, demonstrating that surrogate-assisted optimization can retain solution quality while enabling rapid design iteration. Overall, the results show that CFD-informed optimization improves detection effectiveness and reduces sensor requirements compared to conventional layouts. The proposed framework supports scalable deployment and provides a foundation for integrating optimized sensor networks with digital twin systems for real-time monitoring and risk assessment.

Fangnian Wang, Nicholas Tan Jerome, Thomas Jordan +1

Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.

Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Optimal Sensor Placement Computational Fluid Dynamic Infrastructure Genetic Algorithms

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Jul 10, 2026cs.RO

How Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection: A Safety Gap in Manual Leak Inspection of Hydrogen System Components

The integrity of hydrogen infrastructure relies on reliable leak detection, performed almost exclusively via manual tracer gas sniffing in electrolyzer manufacturing. Although mandated by standards, the lack of spatial probe guidance instructions leaves detection reliability entirely to operator execution, further compromised by sensor signal delays. This study quantifies how sniffer trajectory kinematics affect detection reliability at small-scale pipes and fittings, a near-field regime largely neglected by macroscopic dispersion research. Using a robotically guided test bench to eliminate operator variability, static concentration fields and dynamic trajectory passes were acquired across representative geometries under standardized leak rates (5 vol% hydrogen in nitrogen) and varying scanning velocities. Results demonstrate that scanning velocity and spatial probe orientation strongly dictate detectability. Conventional linear trajectories frequently miss leaks under dynamic conditions, causing severe false negatives. Conversely, geometry-specific routing, such as circumferential plunging paths around sealing points, maintains a high safety margin. From these observations, geometry-specific routing rules and a reduction-factor model for dynamic signal loss are derived. The findings reveal that current standard operating procedures pose a tangible safety risk. To operationalize these rules, a proof-of-concept software pipeline is presented, generating validated trajectories directly from 3D models for visualization in assistance systems.

Christian Masuhr, Arne Wendt, Thorsten Schüppstuhl

Institute of Aircraft Production Technology (IFPT), Hamburg University of Technology (TUHH), Denickestraße 17, Hamburg, 21073, Germany

Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Hydrogen System Components Inspection Traces Geometric Guidance

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Jun 26, 2026cs.LG

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings

Inspired by advances in natural language processing and computer vision, "time-series foundation models" (TSFMs) have recently been introduced with the promise of strong generalization across diverse time-series tasks, including forecasting, classification, and anomaly detection, as well as across domains such as healthcare, climate science, and manufacturing. However, their utility for gas-sensing data remains largely unexplored. To address this gap, this paper systematically evaluates recent TSFMs on electronic nose (E-Nose) data. In particular, we investigate whether embeddings produced by representative TSFMs, including Chronos-2 and MOMENT, provide effective representations for gas identification and concentration prediction. Specifically, we show that fine-tuning is necessary to achieve satisfactory performance on E-Nose data, and fusing TSFM embeddings with representations learned by specialized predictive models can further improve the performance, suggesting both the potential and limitations of current TSFMs for gas-sensing applications.

Taeyeong Choi, Mohammed Kamruzzaman

Department of Information Technology Kennesaw State University Marietta, GA 30060, USA · Department of Agricultural and Biological Engineering University of Illinois Urbana-Champaign Urbana, IL 61801, USA

Time Series Foundation Models Timestep Embeddings Smells Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Concentration

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May 13, 2026physics.chem-ph

Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO $_2$ Carbon Monoxide Sensor with p-n Switching

This work presents a physics-guided machine-learning framework for carbon monoxide concentration inference from experimentally measured resistance transients of a mixed-phase SnO-SnO

_2

material gas sensor exhibiting temperature-dependent p-n switching behavior. Cycle-level transient responses are represented through physically interpretable descriptors and complemented by compact fast Fourier transform (FFT) and discrete wavelet transform (DWT)-based summaries. Using leakage-aware grouped cross-validation, we study both multi-class concentration classification and continuous concentration regression for the p-type and n-type sensing regimes separately. Across both regimes, fused features provide the strongest overall performance, while the physics-guided descriptor block remains highly competitive, indicating that the dominant concentration information is already encoded in physically meaningful transient dynamics. The p-type branch shows the best concentration-class discrimination, with the fused Random Forest classifier reaching approximately

96.5\%

accuracy, whereas the n-type branch yields the best quantitative concentration estimation, with the fused Random Forest regressor achieving an MAE

\approx 1.48

ppm and an R

^2

\approx 0.992

. These results reveal a clear dual-regime behavior: p-type sensing is particularly favorable for classification, whereas n-type sensing is more favorable for high-fidelity regression. More broadly, the study demonstrates that leakage-aware, cycle-level, physics-guided machine learning can extend conventional gas-sensing analysis beyond single-response metrics while preserving physical interpretability

Sani Biswas, Preetam Singh, Amit Kumar Gangwar

Centro de Modelamiento Matem´atico, Universidad de Chile & IRL 2807 - CNRS, Santiago, Chile · CSIR-National Physical Laboratory, Dr. K.S. Krishnan Marg, New Delhi, 110012, India · Department of Chemical Engineering, Biotechnology and Materials, FCFM, Universidad de Chile, Santiago, Chile and ANID - Millenium Science Initiative, Millenium Nuclei of Advanced MXenes for Sustainable Applications (AMXSA), Santiago, Chile

Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Methane Respiratory Motion Physics-Guided Machine Learning

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May 13, 2026cs.RO

Calibration-Free Gas Source Localization with Mobile Robots: Source Term Estimation Based on Concentration Measurement Ranking

Efficient Gas Source Localization (GSL) in real-world settings is crucial, especially in emergency scenarios. Mobile robots equipped with low-cost, in-situ gas sensors offer a safer alternative to human inspection in hazardous environments. Probabilistic algorithms enhance GSL efficiency with scattered gas measurements by comparing gas concentration measurements gathered by robots to physical dispersion models. However, accurately deriving gas concentrations from data acquired with low-cost sensors is challenging due to the nonlinear sensor response, environmental dependencies (e.g., humidity, temperature, and other gas influences), and robot motion. Mitigating these disturbance factors requires frequent sensor calibration in controlled environments, which is often impractical for real-world deployments. To overcome these issues, we propose a novel feature extraction algorithm that leverages the relative ranking of gas measurements within the dynamically accumulated dataset. By comparing the rank differences between gathered and modeled values, we estimate the probabilistic distribution of source locations across the entire environment. We validate our approach in high-fidelity simulations and physical experiments, demonstrating consistent localization accuracy with uncalibrated gas sensors. Compared to existing methods, our technique eliminates the need for gas sensor calibration, making it well-suited for real-world applications.

Wanting Jin, Agatha Duranceau, İzzet Kağan Erünsal +1

Distributed Intelligent Systems and Algorithms Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland

Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Mobile Robots Localization

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May 2, 2026physics.optics

Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments

Laser absorption spectroscopy (LAS) is a well-established technique for non-intrusive measurement of gas species in combustion and atmospheric environments, but conventional methods struggle with multi-species mixtures under dynamic or interference-laden conditions. Overlapping spectral features, noise, and incomplete reference data limit reliability when unknown or weakly absorbing species are present. This dissertation develops diagnostics combining LAS with machine learning (ML) to address these limitations. Deep denoising autoencoders (DDAEs) are applied to shock-tube measurements during high-speed hydrocarbon pyrolysis, improving signal fidelity and detection limits for trace species. A structured unsupervised framework, HT-SIMNet, then mitigates interference from unknown species without full calibration data, using spectral augmentation and a Noise2Noise-inspired scheme to isolate species in reactive systems. Where reference spectra are unavailable, UnblindMix, an autoencoder-based blind source separation method, reconstructs concentrations and spectral signatures directly from mixture data, validated on mixtures of up to eight components. To recover weakly absorbing species masked by broader absorbers, a feature-engineering method based on first derivatives and convolutions selectively highlights minor species. Finally, VOC-certifire combines randomized smoothing with Voigt-based spectral perturbation to provide certifiable classification of volatile organic compounds under varying conditions. All techniques are experimentally validated and benchmarked. The integration of spectroscopic hardware with ML offers a path toward real-time, interference-resilient, reference-free gas detection for combustion science, environmental monitoring, and industrial safety.

Mohamed Sy

King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia

Laser-Induced Breakdown Spectroscopy Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Combustion Control Speech Separation

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Apr 23, 2026cs.LG

A temporal deep learning framework for calibration of low-cost air quality sensors

Low-cost air quality sensors (LCS) provide a practical alternative to expensive regulatory-grade instruments, making dense urban monitoring networks possible. Yet their adoption is limited by calibration challenges, including sensor drift, environmental cross-sensitivity, and variability in performance from device to device. This work presents a deep learning framework for calibrating LCS measurements of PM

_{2.5}

, PM

_{10}

, and NO

_2

using a Long Short-Term Memory (LSTM) network, trained on co-located reference data from the OxAria network in Oxford, UK. Unlike the Random Forest (RF) baseline, which treats each observation independently, the proposed approach captures temporal dependencies and delayed environmental effects through sequence-based learning, achieving higher

R^2

values across training, validation, and test sets for all three pollutants. A feature set is constructed combining time-lagged parameters, harmonic encodings, and interaction terms to improve generalization on unseen temporal windows. Validation of unseen calibrated values against the Equivalence Spreadsheet Tool 3.1 demonstrates regulatory compliance with expanded uncertainties of 22.11% for NO

_2

, 12.42% for PM

_{10}

, and 9.1% for PM

_{2.5}

Arindam Sengupta, Tony Bush, Ben Marner +2

1ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza Cardenal Cisneros, 3, Madrid, 28040, Spain · 2Air Quality Consultants Ltd., 3rd Floor, St. Augustine’s Court, St. Augustine’s Place,2026 Bristol, BS1 4UD, United Kingdom

Air Pollution Deep Learning Framework Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Environment

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Dec 29, 2025cs.CV

Edge-Aware and Content-Adaptive Infrared Gas Leak Detection for Industrial Safety Monitoring

Infrared gas leak detection is important for industrial safety and environmental monitoring, but automatic detection remains challenging because gas plumes are often faint, small, semi-transparent, and weakly bounded. This study proposes an Edge-Aware and Content-Adaptive Feature Fusion Detector (ECAF-Det) for infrared gas leak detection in weak-plume and cluttered thermal scenes. The main methodological contributions of ECAF-Det comprise three task-oriented components. A local--global feature enhancement block preserves fine boundary cues and long-range plume continuity. A multi-scale edge perception module transforms directional-gradient and Gabor-response cues into hierarchical boundary-sensitive structural priors. A content-adaptive sparse routing path aggregation network dynamically regulates multi-scale feature propagation and limits the contribution of less informative cross-scale responses. Experiments on the IIG dataset show that ECAF-Det improves overall and small-plume detection while maintaining moderate computational complexity. On this dataset, ECAF-Det achieves an average precision (AP) of 29.8%, an AP at an IoU threshold of 0.5 AP50 of 84.3%, and a small-object AP of 25.3%. Compared with the Real-Time Detection Transformer with a ResNet-18 backbone (RT-DETR-R18), these values represent improvements of 3.0, 6.5, and 5.4 percentage points, respectively. The model requires 43.7 giga floating-point operations (GFLOPs) and 14.3 M parameters. On the LangGas dataset, ECAF-Det achieves an AP of 36.3% and an AP50 of 68.5%. The AI contribution lies in edge-aware representation learning and content-adaptive sparse feature routing for weak infrared plume perception. The engineering application is automated infrared gas leak detection for industrial safety monitoring, early warning, and remote inspection.

Dongsheng Li, Tianli Ma, Siling Wang +2

School of Mechatronic Engineering, Xi’an Technological University, Xi’an, 710021, Shaanxi, China · School of Electronic Information Engineering, Xi’an Technological University, Xi’an, 710021, Shaanxi, China · Shaanxi Shanhua Coal Chemical Co., Ltd., Weinan, 714104, Shaanxi, China

Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Infrared Small Target Detection Unsupervised Detection Edge-Aware Adaptive Feature Fusion Detection Transformer

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Aug 27, 2025cs.CV

JVLGS: Joint Vision-Language Gas Leak Segmentation

Gas leaks pose severe risks to human health and industrial safety. However, accurate and timely monitoring of gas leaks remains a major challenge. Existing vision-based methods using infrared (IR) imagery are limited by the inherently blurry and non-rigid nature of leak plumes, which reduces detection reliability and precision. To overcome these limitations, this paper proposes a Joint Vision-Language Gas leak Segmentation (JVLGS) framework that integrates the complementary strengths of visual and textual modalities to enhance gas leak segmentation. Recognizing that gas leaks are sporadic and many video frames contain no leakage, JVLGS incorporates an adaptive postprocessing module to effectively suppress false positives caused by noise and non-target objects-a common limitation of existing approaches. Extensive experiments across diverse industrial scenarios demonstrate that JVLGS significantly outperforms state-of-the-art gas leak segmentation methods. Furthermore, it achieves consistently strong performance under both supervised and few-shot learning settings, whereas competing methods typically perform well in only one setting or underperform in both.

Xinlong Zhao, Qixiang Pang, Shan Du

University of British Columbia - Okanagan · University of Central Missouri

Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Video Object Segmentation Computer Vision Infrared

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