Recurrent Neural Network Model with Self-Attention Mechanism for Fault Detection and Diagnosis

Fault detection and diagnosis (FDD) plays an important role in production safety and efficiency. The recurrent neural network (RNN) based FDD method can automatically extract features between input sequences to accomplish end-to-end FDD. RNN based FDD method can be regarded as an encoder-decoder framework. An encoder reads input sequences to generate features with RNN, and a decoder uses the features to recognize fault. Using the final hidden state of RNN is a common approach to obtain features in previous methods. In this paper, we apply Self-attention (SA) mechanism to the gated recurrent unit (GRU), as a kind of RNN, and the GRU-SA based FDD method is proposed. The method is illustrated on Tennessee Eastman process, and experimental results show GRU-SA method can improve FDD performance.

[1]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[2]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[3]  Hao Wu,et al.  Deep convolutional neural network model based chemical process fault diagnosis , 2018, Comput. Chem. Eng..

[4]  Jin Wang,et al.  Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes , 2007, IEEE Transactions on Semiconductor Manufacturing.

[5]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[6]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[7]  Mohieddine Jelali,et al.  Revision of the Tennessee Eastman Process Model , 2015 .

[8]  Jing Yuan,et al.  An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes , 2019, Processes.

[9]  Bo Jin,et al.  Sequential Fault Diagnosis Based on LSTM Neural Network , 2018, IEEE Access.

[10]  Riccardo Muradore,et al.  A PLS-Based Statistical Approach for Fault Detection and Isolation of Robotic Manipulators , 2012, IEEE Transactions on Industrial Electronics.

[11]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[12]  Zhanpeng Zhang,et al.  A deep belief network based fault diagnosis model for complex chemical processes , 2017, Comput. Chem. Eng..

[13]  Si-Zhao Joe Qin,et al.  Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..

[14]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[15]  S. Qin,et al.  Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .

[16]  Bowen Zhou,et al.  A Structured Self-attentive Sentence Embedding , 2017, ICLR.