Abstract Fault diagnosis in aerospace equipment is critical for ensuring flight safety and operational reliability. Existing methods often struggle with dynamic fault propagation and uncertainty under sparse data conditions. This study proposes a DFTA-enhanced belief rule base (DFTA-BRB) model for interpretable fault diagnosis in aerospace equipment. The framework integrates dynamic fault tree analysis (DFTA) to formalize fault propagation mechanisms and employs a belief rule base (BRB) with evidence reasoning (ER) for uncertainty modeling under sparse data. Key innovations include: (1) DFTA-based fault interaction modelling, (2) ER-based diagnostic inference, and (3) parameter optimization via the Projected Covariance Matrix Adaptation Evolution Strategy (P-CMA-ES). Case studies demonstrate that DFTA-BRB significantly improves diagnostic interpretability and reliability under sparse fault data and dynamic failure propagation conditions. The approach effectively bridges dynamic fault mechanism modelling with uncertainty-aware reasoning, providing a systematic and reliable solution for spacecraft fault diagnosis.
Liu et al. (Mon,) studied this question.