The invention discloses a
laser fault diagnosis method based on a neural network. The method comprises the following steps: S10, setting a characteristic coefficient and a basic coefficient in lasers of different degradation types, and calculating to obtain a
driving current; s20, using the normalized
driving current to replace the
driving current, compressing the time length to a set value, performing arrangement to obtain a plurality of data groups simultaneously comprising the
optical power, the normalized driving current, the temperature, the
current threshold and the
wavelength, and dividing the data groups into a neural network
training set and a
test set; s30, an SE module is introduced to construct an SE-LSTM
algorithm, and an Adam optimizer is used to optimize parameters; s40, inputting the neural network
training set in the S20 into the neural
network model in the S30 for training, and outputting a degradation type by using a multi-classification
cross entropy as a
loss function of training; s50, evaluating the trained model by using the
test set in the S20 through the set evaluation index; the invention further discloses a corresponding
system. The attention of the model to important features is significantly improved, and effective and reliable monitoring of the
laser is realized.