The invention discloses a training method and a fault diagnosis method of a
solid oxide fuel
cell system multi-fault diagnosis model based on a graph neural network, and the method comprises the steps: obtaining multi-source
time sequence operation data of an SOFC
system in different operation states, and obtaining a
data set; the operation state comprises a
normal state, a
single fault state and a composite fault state, and the
data set is marked with an operation state
label; preprocessing the sample data in the
data set, generating a node
feature matrix and a corresponding adjacent matrix according to each piece of sample data, and forming a graph structure data set; constructing a graph neural
network model which sequentially comprises a multi-head graph attention layer, a residual graph
convolution layer and a global
pooling classification layer, then taking graph structure data in the graph structure data set as input, taking a corresponding operation state as output, and training the graph neural
network model to obtain a trained multi-fault diagnosis model; the method can be used for multi-fault decoupling diagnosis of the SOFC
system, and diagnosis accuracy and practicability are improved.