A Novel GA-PSO-SVM Model for Compound Fault Diagnosis in Gearboxes With Limited Data

Accurately diagnosing compound faults in gearboxes, where multiple fault modes co-occur, poses significant challenges, particularly when labeled training data is limited. This paper presents a novel gearbox fault diagnosis model integrating advanced signal processing, multi-scale feature extraction, and an optimized Support Vector Machine (SVM). The proposed model employs Variational Mode Decomposition (VMD) for signal denoising and decomposition, with parameters optimized using PSO and permutation entropy as the fitness function. A two-stage feature extraction process selects the most informative IMFs using Pearson correlation analysis and applies Wavelet Packet Decomposition (WPD) to extract multi-scale…

• NN
  National Natural Science Foundation of China

  Award: 52372420