Symmetric recurrent neural network for anomaly detection in industrial process

The rapid development of intelligent manufacturing brings great opportunities and challenges to security protection. Various kinds of security threats may cause serious losses or even disasters, which have become an urgent problem to be solved in the industrial Internet. A novel symmetric recurrent neural network that utilized the adaptability of neural network and the characteristic of gate circuit in Long Short Term Memory (LSTM) network was proposed. Aiming at the problem of low accuracy inanomaly detection for industrial Internet streaming data over time, the forget gate, memory gate and output gate were calculated by different weights and current input data. Then the prediction results were solved by sigmoid activation function, which were used as the input of Gated Recurrent Unit(GRU) network layer to promote the rapid fitting of the current network, so that the better parameters could be obtained in a short time. The last hidden state of LSTM was kept by combining the advantages of LSTM and GRU to take as the input of next layer for GRU, which made the neural network more smooth and maximum retention the parameters of LSTM. The proposed method greatly improved the accuracy of neural network, which could both efficiently and quickly detect the anomalies in industrial Internet. © 2024 CIMS. All rights reserved.