Abstract Regarding the problem that the weak fault features in the vibration signals of aircraft engine gearboxes are difficult to stably extract under strong noise interference and non-stationary conditions, a multi-stage signal processing method oriented toward enhancing weak impact features is proposed. This method builds a full-band multi-scale decomposition framework based on the maximum overlap wavelet packet transform, and combines the Choi-Williams distribution to achieve time-frequency energy reconstruction, in order to improve the focusing ability of fault features in the time-frequency domain. On this basis, the CEEMDAN decomposition, permutation entropy filtering, and adaptive wavelet threshold denoising are introduced to construct a multi-level denoising strategy. Through the collaborative mechanism of “modal decoupling-information screening-directional denoising”, the stable retention and progressive enhancement of weak fault information in the multi-stage processing process are achieved. Experimental results show that the proposed method can effectively improve the structural distinguishability and feature stability of vibration signals in complex noise backgrounds, enable clearer expression of periodic impact features and fault harmonic structures, and achieves a good balance between noise reduction performance and signal fidelity. The research results indicate that the proposed method enhances the detectability of early weak faults in aircraft engine gearboxes, providing a robust signal processing path and engineering application potential for high-reliability condition monitoring and intelligent diagnosis under complex conditions.
Manke Yin (2026) studied this question.