Intelligent Fault Diagnosis and Maintenance Decision Support in Electrical Induction Generators Using Multi-CNN Extreme Ensemble Learning

Abstract

Electrical induction generators are vulnerable to winding faults that can degrade operational reliability and lead to unplanned maintenance. This study proposes a multiple-convolutional neural network (Multi-CNN) extreme ensemble learning framework for intelligent fault diagnosis and maintenance decision support using three-phase current and voltage measurements. Experimental recordings representing healthy operation, inter-turn faults, and inter-winding faults were segmented into non-overlapping 200-sample windows. Hjorth activity, mobility, and complexity were calculated for the three-phase current signals and the three-phase voltage signals, producing 18 features for each of 900 instances. Four convolutional neural network architectures were trained, and their class-probability outputs were combined through an extreme learning machine. Stratified blocked five-fold cross-validation was used to evaluate the models while preserving the chronological structure of the data. The proposed ensemble achieved 98.111% accuracy, 98.146% precision, 98.111% recall, 98.108% F1-score, and 97.167% Matthews correlation coefficient, correctly classifying 883 of 900 out-of-fold instances. It also attained a macro-averaged area under the receiver operating characteristic curve of 0.995. These results demonstrate that Hjorth-based electrical-signal characterization and Multi-CNN ensemble fusion can provide accurate and computationally efficient support for fault identification and predictive maintenance decisions in electrical induction generators.