A two-step transfer learning approach for railway point machine fault diagnosis under small sample conditions

Key Points

• This analysis reveals significant improvements in fault diagnosis accuracy.
• Transfer learning boosted performance by reducing training time on small datasets.
• Employing a two-step approach enhances model adaptability for limited data situations.
• Highlights the need for advanced techniques in railway system maintenance under constraints.