# A Small-Sample Fault Diagnosis Method for High-Voltage Circuit Breaker Spring Mechanisms Based on Multi-Source Feature Fusion and Stacking Ensemble Learning

## TL;DR

This paper introduces a new method for diagnosing faults in high-voltage circuit breakers using sensor fusion and machine learning, achieving high accuracy even with limited data.

## Contribution

A novel small-sample fault diagnosis method using multi-source feature fusion and Stacking ensemble learning for high-voltage circuit breakers.

## Key findings

- The proposed method achieved 96.1% average diagnostic accuracy, outperforming single base models like Random Forest.
- Closing and opening pressures were identified as the most critical features for mechanical fault detection.
- The two-layer Stacking model with SVM, RF, and KNN base classifiers improved performance through algorithm fusion.

## Abstract

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions.