C	Classification module
Ca	Class center of the a-th class
Di	Distance between the sample and class center
DJS	Jensen–Shannon divergence
DKL	Kullback–Leibler divergence
$F_i^t$	Features of the i-th target domain sample
$F_i^s$	Features of the i-th source domain sample
G	Feature extraction module
LCE	the cross-entropy loss
Ld	the total loss
Ls	Discrepancy loss of classifiers
$L_t^i$	the binary cross-entropy loss of the i-th target domain sample
M	Number of fault classes in the source domain
ns	Number of labeled samples
nt	Number of unlabeled samples
na	Number of a-th class samples
$p_{x_i^t}^{M+1}$	Probability that the i-th sample is recognized as a non-shared class.
pc1	Probability distribution of classifier 1’s output
pc2	Probability distribution of classifier 2’s output
t	Threshold
v	Dimension of the Feature extraction module’s output
W	Weighting module
wi	Weight of the i-th sample
$x_i^s$	The i-th sample in the source domain
$x_i^t$	The i-th sample in the target domain
$y_i^s$	Label of the i-th sample in the source domain
$y_i^t$	Label of the i-th sample in the target domain