A parallel deep learning algorithm with applications in process monitoring and fault prediction. (April 2022)

• Record Type: Journal Article
• Authors: Qian, Hong; Sun, Bo; Guo, Yuanjun; Yang, Zhile; Ling, Jun; Feng, Wei
• Is Part Of: Computers & electrical engineering. Volume 99(2022)
• Publication Date: 2022-04
• Subjects: Fault prediction -- Parallel deep learning -- Long-short term memory model -- Convolutional neural network -- Principal component analysis; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107724
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Effective and timely fault detection and status monitoring of the industrial production process is essential to fully guarantee the operational safety. However, massive multi-source heterogeneous data analysis is facing many challenges. This paper proposes a process monitoring model combined with a parallel deep learning algorithm and Principal Component Analysis (PCA) method. Firstly, PCA is applied to realize fault diagnosis and extract fault characteristic variables, thus abnormal conditions can be revealed. In order to reduce the complexity of processing massive data, a parallel deep learning framework consisting of multi-models of convolutional neural network (CNN) and Long-Short Term Memory (LSTM) neural network is proposed to effectively predict the target variable status. Comprehensive experiments are taken under two real-system scenarios, and comparisons are made against four traditional neural network models to demonstrate its practicability and effectiveness. The generated results clearly show that the CNN-LSTM parallel model combined with PCA outperform other popular models due to its merged advantages of accurate time series prediction and effective fault feature extraction. Graphical abstract: Highlights: A parallel deep learning approach with feature extraction and long time series prediction. Multi-source unsupervised faulty variable extraction and detection can be realized. Successfully solve the distribution power grid fault diagnosis in multiple locations.