Machine Learning and Digital Twins for RUL Prediction of DC Semiconductor Circuit Breakers

Key Points

• The aim is to predict the Remaining Useful Life (RUL) of DC semiconductor circuit breakers using advanced machine learning techniques and digital twins.
• Utilized machine learning algorithms to analyze performance data.
• Created digital twins to simulate circuit breaker behavior.
• Applied RUL prediction models to assess lifespan.
• Accurate predictions of the Remaining Useful Life (RUL) for circuit breakers.
• Enhanced reliability and maintenance scheduling based on predictive insights.
• Demonstrated potential cost savings through optimized lifecycle management.