A fault diagnosis method for abnormal valve clearance of marine diesel engines based on adaptive CYCBD and EWPDNet

Traditional methods for diagnosing abnormal valve clearance in marine diesel engines face challenges in noise suppression and feature extraction.

To address these issues, this study proposes a method combining adaptive maximum second-order cyclostationarity blind deconvolution (CYCBD) with an efficient wide path dense network (EWPDNet). First, the envelope harmonic product spectrum is used to estimate the cyclic frequency, while the Alpha evolution algorithm optimizes CYCBD filter length, enabling adaptive noise reduction and feature enhancement. Subsequently, EWPDNet integrates a parallel convolution path and an efficient channel attention module into DenseNet to improve the extraction of complex features.

Experimental results demonstrate that the proposed method achieves over 93% accuracy under various noise conditions, with an average accuracy of 95.88%.

The proposed approach provides a basis for the diagnosis of abnormal valve clearance faults in marine diesel engines.