# Fault Diagnosis of Vehicle Gerboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
###### Abstract  
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. CEEMDAN; wavelet threshold denoising; LT-PCA; SVM; fault diagnosis Article  
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underwent final wavelet noise reduction for effective noise filtration. [PERSON] et al. [5] conducted CEEMDAN decomposition of the original signal, breaking down the intrinsic mode function (IMF) into noise components, noise-dominant components, true signal-dominant components, and true signal components. Discarding the noise-dominant and real signal-dominant components, they reconstructed a noise-canceled signal with the genuine signal component, employing wavelet soft thresholding for noise reduction. [PERSON] et al. [6] applied CEEMDAN to decompose the blast vibration signal into IMF components, further categorizing them into major and minor components based on correlation coefficients. The denoising process involved fixed and soft thresholding techniques applied to the two groups of IMF. [PERSON] et al. [7] employed CEEMDAN to decompose the rolling bearing vibration signal, screening each IMF energy value, reducing wavelet noise in abnormal IMFs, and subsequently reconstructing the signal. The results indicate that employing CEEMDAN with wavelet threshold noise reduction significantly reduces signal noise. However, CEEMDAN decomposition yields various intrinsic mode function (IMF) components, each containing distinct types of noise. During the wavelet noise reduction process, the efficacy varies significantly based on influencing factors such as the choice of threshold functions. Therefore, it is crucial to optimize the noise reduction process. The gearbox is affected by many factors during operation, which generate complex nonlinear vibration signals; therefore, determining how to obtain accurate fault information is particularly important. [PERSON] et al. [8] applied principal component analysis (PCA) to energy and kurtosis values from different intrinsic mode functions (IMFs) for automatic bearing fault identification, demonstrating PCA's effectiveness in early fault detection. [PERSON] et al. [9] utilized PCA to reduce the dimensionality of vibration signals, extracting fault features for training with the DBN algorithm for identification and classification. [PERSON] et al. [10] combined PCA and SVM with cross-validation to classify bearing faults, proving its suitability for engineering applications. To address residual noise influence and effectively identify subtle fault content within limited vibration signals, this article diverges from traditional PCA direct dimensionality reduction.