Research on Rotating Machinery Fault Diagnosis Based on Multi-Strategy Feature Extraction

This study proposes a novel feature extraction method that leverages multi-strategy optimization algorithms to enhance the accuracy and efficiency of rotating machinery fault diagnosis. By introducing an improved slime mold algorithm (LTSSMA), it optimizes the penalty factor and decomposition layers in variational mode decomposition (VMD), achieving more precise signal decomposition. The sample entropy generated by VMD forms the basis of the feature vector, and the nonlinear dimensionality reduction algorithm (t-SNE) is applied to reduce dimensions. Finally, the optimized features are classified using a random forest (RF) model, resulting in an 11.3% improvement in fault diagnosis accuracy compared to traditional methods. This method not only accelerates the diagnostic process but also significantly improves fault identification reliability.