It is crucial to recognize unknown faults for the intelligent fault diagnosis of bogie mechanical transmission components. This is essential to ensure the safety of trains in long-term service. However, the unknown faults are hardly represented accurately because of the influence of vibration signals multipath propagation. This results in a challenge to efficiently construct open-set decision boundaries (OSDB). An open-set fault diagnosis (OSFD) method based on multichannel feature-enhanced placeholder learning is proposed to improve diagnostic reliability. It utilizes the multichannel vibration signals collected by sensors located at different positions to construct a placeholder learning network based on the association of multichannel features. It can effectively improve the association of unknown class presentations and boost the effect of unknown fault identification. Besides, the placeholder learning network based on dummy classifier enhancement is proposed to strengthen the representations of known classes and ensure the generalization of OSDB. The proposed approach is validated with case studies constructed by high-speed train bogie fault datasets and the data challenge public datasets of PHM-Beijing 2024. The results demonstrate that the proposed method can enhance the effectiveness of OSFD.