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

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.

Centrifugal pump impeller defect identification by the improved adaptive variational mode decomposition through vibration signals

“…Mode number k varies in the interval of [2,7], and the quadratic penalty factor a takes value in an interval of [1000,10000]. The range for parameters has been decided based on an extensive literature survey [23,34,41].…”

“…The VMD parameters viz, mode number, and quadratic penalty factor are generally determined based on experience. This greatly affects the performance of VMD and sometimes leads to inaccurate decomposition results [23]. The optimal combination of parameters is the primary requirement for VMD based decomposition.…”

Parametric optimization of FDM using the ANN-based whale optimization algorithm

“…In continuation, the effect of process parameters on the SR, production time (PT), and build volume of printed products are tabulated in Table 1. Some of the researchers have implemented AI algorithms in different research areas for various applications (Chauhan and Vashishtha, 2021; Chauhan et al , 2021; Vashishtha and Kumar, 2021 a , 2021 b , 2022; Vashishtha et al , 2021 Guleria et al 2022 b ).…”

A Robust Health Indicator for Rotating Machinery Under Time-Varying Operating Conditions

“…Xiang et al [25] developed method based on Teager Computed Order(TCO) spectrum and Stacking Auto-encoder(SAE) to extract features adaptively from vibration under variable speed and load conditions. Aside from this, more studies have been conducted and interested readers are referred to [26]- [30]. However, only a few researchers have performed extracting robust HI for the prognosis study under variable operating conditions.…”