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Fault detection of pem fuel cell for vehicle systems using neutral network models
Mahanijah Md Kamala1, Dingli Yu2.
This paper presents the neural network modeling method to perform fault detection for
proton exchange membrane fuel cell dynamic systems under an open-loop scheme. These
methods use a radial basis function neural network and a multilayer perceptron neural
network to perform fault identification. Five types of faults which commonly happened in
the vehicle systems have been introduced to the modified benchmark model developed
by Michigan University. The developed algorithm of RBF and MLP network models are
implemented on Matlab/Simulink environment using the healthy data sets and faulty data
sets obtained from the simulation. All five simulated faults have been successfully detected
where the residual is designed sensitive to fault amplitude as low as +10% of their nominal
values. Thus, it is possible to apply the developed algorithm to real dynamics system of
vehicles for monitoring and maintenance purposes.
Affiliation:
- Universiti Teknologi MARA, Malaysia
- Liverpool John Moores University, United Kingdom
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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