Resumen
Vehicle gearboxes are subject to strong noise interference during operation, and the noise in the signal affects the accuracy of fault identification. Signal denoising and fault diagnosis processes are often conducted independently, overlooking their synergistic potential in practical applications. This article proposes a gearbox fault identification method that integrates improved adaptive modified wavelet function noise reduction, logarithmic transformation on principal component analysis (LT-PCA), and support vector machines (SVMs) to mitigate the influence of noise and feature outliers on fault signal recognition. Initially, to address the issue of interfering signals within the original signal, an innovative adaptive wavelet function optimized by the simulated annealing (SA) algorithm is employed for noise reduction of the main intrinsic mode function (IMF) components decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Subsequently, due to the persistence of high-dimension feature vectors containing numerous outliers that interfere with recognition, the LT-PCA compression and dimensionality reduction method is proposed. Experimental analyses on vehicle gearboxes demonstrate an average fault recognition rate of 96.65% using the newly proposed wavelet noise reduction function and the integrated method. This allows for quick and efficient identification of fault types and provides crucial technical support for related industrial applications.