[Fig. \[CNN\]]{}. In [Fig. \[CNN\]]{}, MaxPool means the Max Pooling layer, AdaptiveMaxPool means the Adaptive Max Pooling layer, and Dropout means the Dropout layer.

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Recurrent Neural Network
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Recurrent neural network (RNN) can describe the temporal dynamic behavior and is very suitable to deal with the time series. However, RNN often exists the gradient vanishing and exploding problems during the training. To overcome these problems, Long Short-term Memory Network(LSTM) was proposed in 1997 [@hochreiter1997long] for processing continual input streams and has made great success in various fields such as NLP, etc. Bi-directional LSTM (BiLSTM) can capture bidirectional dependencies over long distances and learn to remember and forget information selectively. We utilize BiLSTM as the representation of RNN to deal with two types of input data (1D and 2D) for the classification task. The details of BiLSTM are shown in [Fig. \[LSTM\]]{}. Besides, in [Fig. \[LSTM\]]{}, Transpose means transposing the channel and feature dimensions of the input data, and BiLSTM Block means the BiLSTM layer.

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Datasets {#S:4}
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In the field of intelligent fault diagnosis, publicly available datasets have not been investigated in depth. Actually, for comprehensive performance comparisons and evaluation, it is important to gather different 