Research on Remaining Useful Life Prediction of Equipment Based on Digital Twins

TL;DR

This paper introduces a digital twin-based model for predicting equipment's remaining useful life to improve maintenance planning.

Contribution

The novel contribution is an integrated learning-based RUL prediction method using digital twin technology.

Findings

Digital twin technology enables real-time online RUL prediction for equipment.

The proposed method demonstrates accuracy and robustness in experiments.

An engineering implementation plan for online RUL prediction is presented.

Abstract

Remaining Useful Life (RUL) prediction is a key factor in fault diagnosis, prediction, and health management (PHM) during equipment operation and service. Its purpose is to predict the time interval from the current moment to the complete failure of the equipment, serving as the basis for condition-based maintenance strategies. Effective RUL prediction enables the scheduling of maintenance plans in advance, thereby reducing equipment downtime and safety incidents. The RUL prediction of equipment and its critical components is an important means of fault diagnosis and prediction. Real-time and accurate RUL prediction results are prerequisites for implementing preventive maintenance, condition-based maintenance, and failure-based maintenance strategies, allowing the identification of optimal maintenance timing. This constitutes a crucial aspect of precise equipment support. The real-time, high-efficiency communication of digital twin technology can support real-time online RUL prediction for equipment. This paper introduces digital twin technology and constructs a digital twin-based RUL prediction model for equipment. The study proposes an integrated learning-based RUL prediction method for equipment, validated through experiments to demonstrate its accuracy and robustness. Finally, this paper presents an engineering implementation plan for online RUL prediction of equipment based on digital twins.

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