Marine motor fault diagnosis based on CEEMDAN and BRECAN under strong noise conditions

Abstract：Objectives The background noise in the engine room during actual ship navigation leads to poor accuracy in fault diagnosis methods. To address this issue, proposes a ship motor fault diagnosis method based on CEEMDAN and RECAN. Methods Firstly, the noisy motor fault signal is decomposed into multiple intrinsic mode components (IMFs) through adaptive noise complete ensemble empirical mode decomposition (CEEMDAN), and the noise dominant signal and information dominant signal in the IMF are divided based on detrended fluctuation analysis. Empirical wavelet transform (EWT) is used to denoise the noise dominant signal; Then, construct a BRECAN model, use network parameters to model and fit the interference of noise on model training, and guide the network to extract fault differential features through the RECA module; Finally, the effectiveness of the proposed method was verified through a motor fault simulation experimental platform. Results The results show that the proposed method can achieve accurate diagnosis of ship motor faults under strong noise, and still maintain a diagnostic accuracy of over 90% under . Conclusions The research results provide reference for the diagnosis of ship motor faults under strong noise.