Decision-constrained class weighting in multi-classifier evaluation for compound fault diagnosis

Abstract Aero-engines have complex structures and operate under harsh conditions, making the rotating components of their power transmission units prone to failures that compromise operational safety. While existing intelligent fault diagnosis algorithms primarily address single-condition challenges, such as variable operating conditions or class imbalance, few can stably handle compound conditions where these issues coexist. To resolve this problem, this manuscript proposes a comprehensive solution: (1) a decision-constrained class weighting strategy is adaptively applied to class imbalance to enhance diagnostic performance under class imbalance; (2) multi-classifier consensus constraints are then integrated to ensure diagnostic reliability through voting-based result alignment; (3) furthermore, an inter-class separation and intra-class aggregation strategy is used to amplifies feature discriminability to further improve model performance; (4) Wasserstein distance minimization is employed to reduce cross-domain distribution discrepancies, strengthening domain adaptation capability. Validated on planetary gearbox and ultra-high-speed aerospace bearing test bench datasets, the method achieves consistent fault classification accuracy above 92%, demonstrating robust and high-precision transfer diagnosis for rotating components under composite conditions. This establishes a solid foundation for intelligent aero-engine health management.