Research on Fault Diagnosis Method with Adaptive Artificial Gorilla Troops Optimization Optimized Variational Mode Decomposition and Support Vector Machine Parameters

To address the issue of intelligent optimization algorithms being prone to local optima, resulting in insufficient feature extraction and low fault-type recognition rates when optimizing Variational Mode Decomposition and Support Vector Machine parameters, this paper proposes a fault diagnosis method based on an improved Artificial Gorilla Troops Optimization algorithm. The Artificial Gorilla Troops Optimization algorithm was enhanced using Logistic chaotic mapping, a linear decreasing weight factor, the global exploration strategy of the Osprey Optimization Algorithm, and the Levy flight strategy, improving its ability to escape local optima, adaptability, and convergence accuracy. This algorithm was used to optimize the parameters of Variational Mode Decomposition and Support Vector Machine for fault diagnosis. Experiments on fault diagnosis with two datasets of different sample sizes showed that the proposed method achieved a diagnostic accuracy of no less than 98% for samples of varying sizes, with stable and reliable results.