Data inherent assessment of electrical equipment by failure pattern and self-evaluation decision-making algorithm

The reliability and safety of power systems strongly depend on the accurate condition assessment of their apparatuses, such as circuit breakers. However, measurement data are often uncertain, noisy, and may contain outliers, leading to unreliable analysis and incorrect maintenance decisions. To address this issue, this paper proposes a self-evaluation decision-making algorithm (SEDMA) based on an inherent base evaluation concept, which enables each piece of equipment to assess its own condition relative to its historical behavior without relying on external references. The proposed framework integrates fuzzy logic and entropy-based degradation modeling to capture both physical deterioration and data uncertainty. It includes an improved data preprocessing stage for outlier detection and removal, followed by a self-comparison process that quantifies the wear-out and failure patterns of equipment. Validation on circuit breaker data demonstrates that SEDMA achieves stable and interpretable condition assessment results, with less than 5% variation under parameter sensitivity analysis and improved robustness compared to statistical and Bayesian baselines. The algorithm operates with linear computational complexity, making it suitable for large-scale and real-time condition monitoring applications. The findings highlight that the proposed SEDMA provides a physically meaningful, self-adaptive, and computationally efficient framework for reliability-centered maintenance of power systems.