Abstract
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.
Highlights
In the case of a small amount of experimental data, the classification performance based on support vector machine (SVM) is significantly better than that based on k-nearest neighbors (KNN), random forest (RF), and decision tree (DT)
The IMF-H is proposed as a new feature vector for the vibration signals of metal particle fault, and the fault diagnosis method proposed is based on the APAPSO-variational mode decomposition (VMD) method and SVM
The variational mode decomposition method based on the particle swarm optimization with adaptive parameter adjustment is applied to decompose and reconstruct the original vibration signal of metal particle faults, which can better display the fault characteristic information than the original VMD method and the empirical mode decomposition (EMD) method, improving the quality of extracted fault feature
Summary
Accurate identification of the size and quantity of metal particles can provide effective early warning of the severity of equipment fault and provide data reference for later disassembly and maintenance, thereby ensuring the safe operation of GIL/GIS. Domestic and foreign research about the metal particle fault in GIL/GIS is mainly focused on analyzing the difference between the metal particle fault and other mechanical faults, and only a few scholars have undertaken research involving the identification and diagnosis of the size and quantity of metal particle faults [4,5,6,7]. In Reference [7], Zhang et al collected discharge signals of linear metal particles that were detected by the conventional pulse current detector and ultrasonic detector and compared the maximum apparent discharge volume and ultrasonic pulse frequency of linear metal particles with the simulation results to estimate the size of linear metal particles. The experimental value of the apparent discharge quantity of particles was quite different from the simulated value, and the proposed size estimation algorithm had not been experimentally verified
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