期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 19, 期 2, 页码 1977-1987出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2022.3198670
关键词
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
This article proposes an end-to-end, deep hybrid network-based, short-term, multivariate time-series prediction framework for industrial processes. The framework extracts nonlinear variate correlation features using the maximal information coefficient, eliminates data uncertainty with a convolutional neural network, achieves step-ahead prediction using a bidirectional gated recurrent unit network, and optimizes the model's learning rate with an optimized Bayesian optimization method. The comparison with other state-of-the-art methods demonstrates the superiority of the proposed framework in noisy IIoT environments.
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.
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