  Considering the models that apply the contextual information of time-series
data could improve the fault diagnosis performance, some neural network
structures such as RNN, LSTM, and GRU were proposed to model the fault
diagnosis effectively. However, these models are restricted by their serial
computation and hence cannot achieve high diagnostic efficiency. Also the
parallel CNN is difficult to implement fault diagnosis in an efficient way
because it requires larger convolution kernels or deep structure to achieve
long-term feature extraction capabilities. Besides, BERT model applies absolute
position embedding to introduce contextual information to the model, which
would bring noise to the raw data and therefore cannot be applied to fault
diagnosis directly. In order to address the above problems, a fault diagnosis
model named deep parallel time-series relation network(DPTRN) has been proposed
in this paper. There are mainly three advantages for DPTRN: (1) Our proposed
time relationship unit is based on full multilayer perceptron(MLP) structure,
therefore, DPTRN performs fault diagnosis in a parallel way and improves
computing efficiency significantly. (2) By improving the absolute position
embedding, our novel decoupling position embedding unit could be applied on the
fault diagnosis directly and learn contextual information. (3) Our proposed
DPTRN has obvious advantage in feature interpretability. We confirm the effect
of the proposed method on four datasets, and the results show the
effectiveness, efficiency and interpretability of the proposed DPTRN model.
