Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects

Area of Science:

• Industrial Internet of Things (IIoT)
• Artificial Intelligence
• Data Science

Background:

• Time-series prediction is vital for intelligent control and management in the IIoT.
• Traditional methods struggle with the increasing complexity of IIoT data.
• Deep learning offers novel solutions for IIoT time-series prediction challenges.

Purpose of the Study:

• To survey deep learning-based time-series prediction methods for IIoT.
• To identify and analyze key challenges in IIoT time-series prediction.
• To propose a framework for state-of-the-art solutions and discuss practical applications.

Main Methods:

• Comprehensive literature review of deep learning techniques applied to IIoT time-series prediction.
• Analysis of existing methods and identification of current challenges.
• Framework proposal for advanced solutions and summary of real-world use cases.

Main Results:

• Deep learning methods show significant promise in addressing IIoT time-series prediction complexities.
• Identified challenges include data heterogeneity, scalability, and interpretability.
• A framework is proposed to guide the application of advanced solutions.

Conclusions:

• Deep learning is a powerful tool for enhancing IIoT time-series prediction.
• Future research should focus on extensible knowledge mining for complex IIoT tasks.
• The proposed framework aids in practical applications like predictive maintenance and supply chain management.