报告详情
Few-Shot Mechanical Fault Diagnosis of High-Voltage Circuit Breakers Using Prior Knowledge-Constrained Wavelet-Based Multi-Frequency Feature Fusion
编号：39
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更新：2025-10-11 22:28:54 浏览：3次
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摘要
Data-driven models for mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) can extract health information from monitoring data. However, in practical applications, the scarcity of fault samples and severe noise interference significantly limit their feature representation ability and generalization performance. Moreover, most existing data-driven approaches overlook the underlying physical mechanisms, lacking fault-mechanism constraints, which reduces their interpretability and credibility. To address these issues, this paper proposes a few-shot fault diagnosis framework for HVCBs based on prior knowledge-constrained wavelet-based multi-frequency feature fusion. First, prior knowledge of HVCB mechanical fault diagnosis is established through excitation–response mechanism modeling, signal feature extraction rules, and simulation analysis. Second, a wavelet-based multi-frequency feature fusion network is introduced, which not only optimizes the basic features extracted by the backbone network but also incorporates frequency-domain features from different scales via wavelet transforms, thereby enhancing subtle feature representation. Third, prior knowledge constraints derived from physical processes and mechanism analysis are embedded into the fusion network, complementing the automatically extracted features of deep learning and mitigating the limitations of purely “black-box” data-driven models. Finally, a joint loss optimization function is employed to alleviate class imbalance, and metric learning is incorporated to enable effective few-shot classification. Experimental results demonstrate that the proposed method achieves a diagnostic accuracy of 98.67% and maintains high stability and generalization under conditions of noise interference and sample imbalance, highlighting its strong potential for engineering applications.
关键词
high-voltage circuit breaker (HVCB); fault diagnosis; prior knowledge; wavelet-based multi-frequency feature fusion; physics-guided integration
报告人
Yanxin Wang
Assistant Professor State Key Laboratory of Electrical Insulation and Power Equipment； Department of Electrical Engineering； Xi’an Jiaotong University稿件作者
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重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
10月12日 2025
初稿截稿日期
-
10月20日 2025
注册截止日期
主办单位
IEEE西南交通大学IAS学生分会
承办单位
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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