The reliability of urban rail train doors is critical for passenger safety and operational efficiency under high-frequency service. However, the coexistence of sudden faults (e.g. obstruction jamming) and progressive degradations (e.g. component wear) presents significant maintenance challenges. Existing studies often focus on a single fault type, limiting both real-time response and long-term health assessment. To address this issue, a self-consistent multi-scenario decision method is proposed for fault diagnosis and health assessment. Based on urgency and evolutionary characteristics, operation scenarios are classified into healthy, emergency, and fault-tolerant modes, forming a unified mechanism throughout the door’s life cycle. During door motion, the 3-sigma rule combined with long short-term memory network enables real-time detection of sudden faults in emergency scenarios. After each motion, multi-domain motor current features are used to construct a health indicator (HI) via a denoising autoencoder, further corrected using the local outlier factor and least squares method. Thresholds are dynamically adjusted through the Chebyshev’s inequality for multi-level health assessment in fault-tolerant scenarios. Validation on a digital twin platform demonstrates a self-consistency score of 0.9826, surpassing comparison methods. The proposed method provides an integrated approach for fault diagnosis, degradation monitoring, and preventive maintenance of urban rail train doors.