ArticleSensors (Basel, Switzerland)2023
PEMFCs Model-Based Fault Diagnosis: A Proposal Based on Virtual and Real Sensors Data Fusion.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Real-time PEM fuel cell fault classification in smart manufacturing plant by using uncertainty aware federated deep learning.Scientific reports · 2026Article
- Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells.Sensors (Basel, Switzerland) · 2024Article
- Data-Driven Power Prediction for Proton Exchange Membrane Fuel Cell Reactor Systems.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Proton Exchange Membrane Fuel Cells (PEMFCs) are critical components in renewable hybrid systems, demanding reliable fault diagnosis to ensure optimal performance and prevent costly damages. This study presents a novel model-based fault diagnosis algorithm for commercial hydrogen fuel cells using LabView. Our research focused on power generation and storage using hydrogen fuel cells. The proposed algorithm accurately detects and isolates the most common faults in PEMFCs by combining virtual and real sensor data fusion. The fault diagnosis process began with simulating faults using a validated mathematical model and manipulating selected input signals. A statistical analysis of 12 residues from each fault resulted in a comprehensive fault matrix, capturing the unique fault signatures. The algorithm successfully identified and isolated 14 distinct faults, demonstrating its effectiveness in enhancing reliability and preventing performance deterioration or system shutdown in hydrogen fuel cell-based power generation systems.
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Registered trials
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