機械学習を用いた車輪とレール間の接触状態の推定

Key Points

• The central aim is to estimate the contact state between train wheels and rails using machine learning.
• Applied machine learning techniques to estimate contact states in real-time.
• Addressed challenges in previous studies using state space theory.
• Investigated the feasibility of the new estimation method.
• Machine learning estimates improve the understanding of wheel-rail contact states.
• Real-time application of these techniques is found to enhance safety assessments.
• The proposed method simplifies parameter and gain adjustments compared to traditional techniques.