To address the issue of reduced fault diagnosis accuracy caused by insufficient samples in laboratory datasets, this study proposes an improved Transfer Component Analysis (TCA) algorithm with dynamic kernel parameter adjustment, combined with Local Mean Decomposition (LMD). Firstly, the original signals are decomposed using LMD, and representative signal components are reconstructed based on the Pearson’s correlation coefficient to enhance feature representativeness. Then, multidimensional features, including Root Mean Square (RMS), kurtosis, and main frequency (MF), are extracted from the reconstructed signals to comprehensively reflect signal characteristics in terms of energy distribution, impact properties, and frequency structure. Subsequently, a dynamic kernel parameter adjustment strategy is incorporated into TCA to adaptively optimize the kernel parameters, effectively reducing the distribution discrepancy between the source and target domains and enhancing the generalization capability of cross-domain feature transfer. Finally, a Least Squares Support Vector Machine (LSSVM) classifier is employed to perform fault diagnosis on the reconstructed features. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic accuracy than traditional approaches under various operating conditions, especially when signals are complex and distribution differences are large, showing strong robustness and adaptability.
Liu et al. (Thu,) studied this question.