Logistics equipment condition monitoring and prediction based on digital twin and machine learning.

The system is highly predictive within the 24-120 hour range, with a decreasing trend observed towards both ends-small lead times (0–24h) are infrequent (12 units), showing the system detects the majority of the degradations at early stages, while predictions after 168 hours become sporadic as of the increased uncertainties in long-term predictions 54 .

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