An effective health indicator for the Pelton wheel using a Levy flight mutated genetic algorithm

Abstract

Abstract Fluctuations in the head, discharge, and contaminants in the flow can damage parts of the Pelton wheel. An artificial intelligence technique has been investigated for the automatic detection of bucket faults in the Pelton wheel. Features sensitive to defect conditions are extracted from the raw vibration signal and its variational mode decomposition (VMD). The issue of slow convergence speed of the genetic algorithm during optimization is duly addressed by implementing a Levy flight mutated genetic algorithm (LFMGA) while finding the optimal parameters (regularization parameter and kernel function) of a support vector machine (SVM). The efficacy of the proposed LFMGA is tested against different optimization benchmark functions. The results indicate that the proposed algorithm is stable on the basis of the small standard deviation. Using optimized SVM parameters, the SVM model is trained to prepare a classification model with 10-fold cross-validation. After training, the SVM model is tested for fitness evaluation. The overall recognition rate of the SVM model for identification of defects is found to be 98.84% with training time 27.06 s per iteration. A healthy condition is also compared with splitter wear, added mass defect, and missing bucket conditions separately using the VMD–SVM model and shows a recognition rate of 99.17%, 98.33%, and 98.12%, respectively.