Remaining Useful Life Prediction Based on Deep Learning: A Survey.

Area of Science:

• Engineering
• Computer Science
• Data Science

Background:

• Remaining Useful Life (RUL) is a critical metric for essential equipment health management.
• Traditional physics-based and data-driven methods for RUL prediction face challenges such as complexity and limited accuracy.
• Deep learning techniques have emerged as a promising approach to enhance RUL prediction.

Purpose of the Study:

• To provide a comprehensive survey of deep-learning-based RUL prediction methods.
• To establish a unified framework for analyzing deep learning models in RUL prediction.
• To identify challenges and future research directions in the field.

Main Methods:

• Literature review of deep-learning-based RUL prediction.
• Proposal of a unified framework for RUL prediction models.
• Comparative analysis of different deep learning models and estimation processes.
• Examination of RUL prediction under specific constraints like limited labeled data.

Main Results:

• Deep learning models offer significant improvements over traditional methods for RUL prediction.
• A structured review under a unified framework categorizes existing approaches.
• Analysis highlights the performance variations across different deep learning architectures.
• Specific challenges, including limited data scenarios, are addressed.

Conclusions:

• Deep learning is a powerful tool for advancing RUL prediction accuracy and efficiency.
• Further research is needed to address challenges like data scarcity and model interpretability.
• The survey provides a valuable resource for researchers and practitioners in equipment health management.