Multivariate Time-Series Prediction in Industrial Processes via a Deep Hybrid Network Under Data Uncertainty

作者：Yao, Yuantao^*; Yang, Minghan; Wang, Jianye; Xie, Min^*

来源：IEEE Transactions on Industrial Informatics, 2023, 19(2): 1977-1987.

DOI：10.1109/TII.2022.3198670

• Task analysis
• Time series analysis
• Uncertainty
• Predictive models
• Monitoring
• Logic gates
• Industrial Internet of Things
• Data uncertainty
• deep hybrid networks
• hyperparameter optimization
• industrial Internet of Things (IIoT)
• multivariate time-series prediction

With the rapid progress of the industrial Internet of Things (IIoT), reducing data uncertainty has become a critical issue in predicting the development trends of systems and formulating future maintenance strategies. This article proposes an end-to-end, deep hybrid network-based, short-term, multivariate time-series prediction framework for industrial processes. First, the maximal information coefficient is adopted to extract the nonlinear variate correlation features. Second, a convolutional neural network with a residual elimination module is designed to eliminate data uncertainty. Third, a bidirectional gated recurrent unit network is connected in a time-distributed form to achieve step-ahead prediction. Last, an optimized Bayesian optimization method is adopted to optimize the model's learning rate. A comparison with other state-of-the-art, deep learning-based, time-series prediction methods in the case study illustrates the superiority of the proposed framework in noisy IIoT environments.

• 单位
  中国科学院