A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis

by   Chun Yang, et al.

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(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. Our model outperforms other methods on both TE and KDD-CUP99 datasets which confirms the effectiveness, efficiency and interpretability of the proposed DPTRN model.


page 11

page 12


A new rotating machinery fault diagnosis method based on the Time Series Transformer

Fault diagnosis of rotating machinery is an important engineering proble...

Improving Position Encoding of Transformers for Multivariate Time Series Classification

Transformers have demonstrated outstanding performance in many applicati...

Fault detection and diagnosis of batch process using dynamic ARMA-based control charts

A wide range of approaches for batch processes monitoring can be found i...

Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features

Deep learning techniques have become prominent in modern fault diagnosis...

Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection

Early fault detection using instrumented sensor data is one of the promi...

Causal Disentanglement Hidden Markov Model for Fault Diagnosis

In modern industries, fault diagnosis has been widely applied with the g...

Do We Really Sample Right In Model-Based Diagnosis?

Statistical samples, in order to be representative, have to be drawn fro...

Please sign up or login with your details

Forgot password? Click here to reset