报告详情
Deep Reinforcement Learning-Guided Adaptive Feature Extraction with Transformer Classification for Variable Speed Bearing Fault Diagnosis
编号：78
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更新：2025-06-26 15:37:47 浏览：125次
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报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
The inherent non-stationarity of vibration signals from variable-speed rotating machinery poses significant challenges for traditional bearing fault diagnosis methods. This paper proposes a novel framework that synergistically integrates Deep Deterministic Policy Gradient (DDPG) optimization with an Adaptive Multi-scale Feature Extraction Network (AMFEN), self-attention mechanism and Transformer-based classification to address these challenges. The proposed methodology employs DDPG to adaptively optimize Vold-Kalman filter parameters for robust signal decomposition, eliminating the need for manual parameter tuning. Subsequently, 5 parallel AMFEN strategies extract complementary features from multiple domains, capturing comprehensive fault characteristics. A self-attention mechanism then intelligently selects the most discriminative features, reducing the dimensionality from 320 to 48 while preserving diagnostic information. Finally, a Transformer model performs fault classification, leveraging its superior capability in capturing feature dependencies and temporal patterns. Experimental validation on BGCF dataest demonstrates the framework's superior performance, achieving 97% diagnostic accuracy under variable speed conditions.
关键词
Bearing fault diagnosis, variable speed machinery, Vold-Kalman filter, AMFEN, Self-Attention mechanism, Transformer
报告人
Liren Pan
Assistant Lecturer Zhenjiang Technician College Jiangsu Province稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月26日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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