Industrial Time Series Data Forecasting of LSTM Neural Network Based on Attention Mechanism. (July 2020)

• Record Type: Journal Article
• Authors: Jiang, Zhiwei; Peng, Xiudong; Sun, Yuanfeng; Zhu, Mengyun
• Is Part Of: Journal of physics. Volume 1601:Number 3(2020)
• Publication Date: 2020-07
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1601/3/032024
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: At present, the maintenance mode of industrial equipment is still based on regular maintenance and after-the-fact maintenance, and with the development of industrial production intelligence, the production data of equipment has increased dramatically. In order to reasonably carry out maintenance activities in the use stage of CNC machine tools and other industrial equipment, this paper proposes an industrial time series data prediction method based on LSTM of attention mechanism. Firstly, based on the LSTM recurrent neural network, combined with the complex historical data of CNC machine tools, the overall characteristics of important time series are obtained. Secondly, the mechanism of attention analysis is introduced, and the algorithm of network structure design and prediction process is given. Finally, the accuracy of prediction is compared with experiments with standard BP neural network. The experimental results show that, compared with the standard BP neural network, the LSTM model based on the attention mechanism has higher prediction accuracy, which verifies the effectiveness of the method.