Feature-engineered ensemble learning methods for insulator fault detection based on ultrasound signals

Abstract

This paper presents a feature-engineered ensemble method for fault detection in medium-voltage insulator ultrasound signals. We develop a feature extraction framework that captures time-domain, frequency-domain, wavelet-domain, and envelope characteristics of ultrasonic emissions from porcelain insulators under various conditions: normal operation, artificial contamination, and simulated lightning damage. A robust signal augmentation methodology enhances classification robustness while preserving class-discriminative properties. We evaluate standard ensemble approaches (random forest, extreme gradient boosting, and light gradient boosting) and introduce two novel algorithms: EvoBagging, which employs evolutionary operators to optimize bootstrap samples, and proximal policy optimization bagging (PPOBagging), which leverages reinforcement learning through proximal policy optimization to construct ensembles via sequential decision-making. Experimental results demonstrate that our feature engineering approach significantly outperforms raw signal classification (91.67% vs. 55.00% accuracy). The proposed PPOBagging algorithm achieves the highest performance with 94.17% accuracy and a macro-F1 score of 0.9412, surpassing conventional methods. Comparative analysis against convolutional kernel transforms (standard, mini, and multi random convolutional kernels) reveals that while these methods offer competitive accuracy (up to 88.33%), they fall short of our feature-engineered ensemble approach in both performance and interpretability. Comparison against deep learning methods also reaffirms the usefulness of the proposed feature engineering technique. This work contributes practical tools for non-destructive monitoring of power distribution systems, enabling proactive maintenance strategies to prevent failures.