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A diagnosis scheme using the Hurst exponent for metal particle faults in GIL/GIS is proposed to improve the accuracy of classification and identification. First, the diagnosis source signal is the vibration signal generated by the collision of metal particles in the electric field. Then, the signal is processed via variational mode decomposition (VMD) based on particle swarm optimization with adaptive parameter adjustment (APA-PSO). In the end, fault types are classified and identified by an SVM model, whose feature vector is composed of the Hurst exponents of each intrinsic mode function (IMF-H). Extensive experimental data verify the effect of this new scheme. The results exhibit that the classification performance of SVM is significantly improved by the new feature vector. Furthermore, the VMD based on APA-PSO with adaptive parameter adjustment can effectively enhance the decomposition quality.