# Fault Diagnosis of Vehicle Gerboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
###### Abstract
Instead, it adopts the LT-PCA feature dimensionality reduction method, incorporating data compression and dimensionality reduction in the fault feature classification process as the subsequent step in fault identification. Fault diagnosis methods encompass model-based approaches [11], signal-based methods [12], and data-driven methods [13]. Model-based methods detect faults via the construction of accurate models; however, challenges arise due to parameter uncertainty factors within the system, impacting the overall model's accuracy and, consequently, fault diagnosis. Traditional signal processing methods involve time-domain, frequency-domain, and time-frequency-domain analyses. Time-domain and frequency-domain analyses may face limitations in identifying non-stationary signals, requiring further analysis. Alternatively, time-frequency-domain analyses are tailored for complex systems, facilitating the identification of vibration signal characteristics and achieving effective vibration data classification and fault detection [14]. Signal classification methods, such as wavelet transform, empirical modal decomposition (EMD), and Fourier transform (FT), can be employed for signal analysis and classification [15; 16]. Data-driven fault diagnosis methods, which are not dependent on precise physical models, conduct fault monitoring by analyzing input and output data, making them well-suited for complex, non-linear, and time-varying systems. Common approaches in this category include support vector machines and neural networks [17; 18]. [PERSON] et al. [19] introduced a fault detection and diagnosis method based on support vector machines (SVMs). This approach involves fault classification through the generation of sampling data from sampled signals and subsequent identification of these sampled signals. While exhibiting superior performance compared to other algorithms, its effectiveness relies heavily on the extracted feature information. [PERSON] et al. [20] devised a method that combines EMD and PSO-SVM for fault feature identification. Experimental evidence supports the algorithm's effectiveness in identifying gear failure types under varying loads. [PERSON] et al. [21] proposed a PSO-SVM hybrid model based on sensitive dimensionless parameters, exhibiting good classification accuracy and efficiency by optimizing kernel parameter settings in SVM training. [PERSON] et al. [22] introduced an intelligent fault diagnosis method for bearings, employing the Grey Wolf Optimization support vector machine. This method outperforms the SVM model in diagnostic effectiveness, although there is room for improvement in the optimization accuracy of GWO. Therefore, this article introduces a signal-denoising algorithm that optimizes and enhances the wavelet threshold function through simulated annealing (SA), combined with CEEMDAN. This innovative approach addresses data loss issues associated with the over-flatness of traditional soft threshold functions after noise reduction and mitigates pseudo-interference from local oscillations in hard threshold functions during the noise reduction process. Following denoising, the LT-PCA method is implemented to compress and downscale the reconstructed signal's fault features. This strategy effectively minimizes the impact of discrete points within signal features in comparison to conventional downscaling methods. Finally, the Northern Goshawk Optimization (NGO) algorithm is employed to optimize support vector machine (SVM) parameters for the fault detection and classification of gearboxes.