A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis
- URL: http://arxiv.org/abs/2112.03405v1
- Date: Fri, 3 Dec 2021 08:24:59 GMT
- Title: A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis
- Authors: Chun Yang
- Abstract summary: A fault diagnosis model named deep parallel time-series relation network(textitDPTRN) has been proposed in this paper.
Our model outperforms other methods on both TE and KDD-CUP99 datasets.
- Score: 7.127292365993219
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 industrial
process 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(\textit{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(\textit{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. Our model outperforms other methods on both TE and KDD-CUP99
datasets which confirms the effectiveness, efficiency and interpretability of
the proposed DPTRN model.
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