fault diagnosis and prognosis. It is impossible to cover all the published models since there is currently no open source community in this field. Therefore, we switch to test the performance of four categories of representative models (MLP, AE, CNN, and RNN) embedding some advanced techniques. It should be noted that DBN is also another commonly used DL methods for fault diagnosis, but we do not add it into this code library due to that the fact the training way of DBN is much different from those four categories.

MLP
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Multilayer Perception (MLP) [@rumelhart1985learning], which was a fully connected network with one or more hidden layers, was proposed in 1987 as the prototype of an artificial neural network (ANN). With such a simple structure, MLP can complete some easy classification tasks such as MNIST. But as the task becomes more complex, it will be hard to train because of the huge amount of parameters. MLP with five fully connected layers and five batch normalization layers is used in this paper for the one dimension (1D) input data. The structure and parameters of the model are shown in [Fig. \[MLP\]]{}. Besides, in [Fig. \[MLP\]]{}, FC means the fully connected layer, 