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.

水头、排放和流量中的污染物的波动会损坏 Pelton 轮的部件。已经研究了一种人工智能技术，用于自动检测 Pelton 轮中的铲斗故障。从原始振动信号及其变分模式分解 (VMD) 中提取对缺陷条件敏感的特征。通过在寻找支持向量机 (SVM) 的最佳参数（正则化参数和核函数）的同时实施 Levy 飞行变异遗传算法 (LFMGA)，适当地解决了遗传算法在优化过程中收敛速度慢的问题。所提出的 LFMGA 的功效针对不同的优化基准函数进行了测试。结果表明，该算法在小标准差的基础上是稳定的。使用优化的 SVM 参数，训练 SVM 模型以准备具有 10 倍交叉验证的分类模型。训练后，对 SVM 模型进行适应度评估测试。发现用于识别缺陷的 SVM 模型的整体识别率为 98.84%，每次迭代的训练时间为 27.06 s。还使用 VMD-SVM 模型将健康状况与分流器磨损、增加质量缺陷和铲斗缺失状况分别进行比较，识别率分别为 99.17%、98.33% 和 98.12%。