A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems

A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems

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In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of t...

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Bibliographic Details
Main Authors: Chen, C. (Author), Chen, Z. (Author), Dong, J. (Author), Li, M. (Author), Li, X. (Author), Peng, J. (Author), Peng, Z. (Author), Rao, M. (Author), Xiong, K. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
BP neural network
BP neural networks
Carbon
Data-driven fault diagnosis
Deposition
Failure analysis
Fault detection
fault diagnosis
Faults diagnosis
Fuel cell system
heat exchanger rupture
Heat exchanger rupture
Heat exchangers
Learning algorithms
Neural networks
Principal component analysis
Principal-component analysis
SOFC
Solid oxide fuel cells (SOFC)
Solid-oxide fuel cell
Support vector machines
Support vector machines algorithms
the reformer carbon deposition
The reformer carbon deposition
Online Access:View Fulltext in Publisher

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