报告详情
Intelligent Fault Diagnosis of Dynamic Equipment in Public Auxiliary Systems Based on Multimodal Deep Q-Networks
编号：83
访问权限：仅限参会人
更新：2025-06-26 15:42:34 浏览：134次
口头报告
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]
暂无文件
摘要
Abstract—Aiming at the challenges of insufficient single-signal feature representation capability and inefficient multimodal information fusion for fault diagnosis of rotating equipment in public and auxiliary systems, this paper proposes an intelligent fault diagnosis method based on multimodal information fusion depth Q network (MIFDQN). The method extracts multimodal features by designing a cross-modal attention encoder, fusing vibration signals and sound signals, and combining them with an improved stacked self-encoder deep Q-network for fault classification decision. The proposed method uses continuous wavelet transform (CWT) to convert one-dimensional time-domain signals into two-dimensional time-frequency representations; then the cross-modal cross-attention mechanism is used to realize the deep fusion of vibration and sound signals; finally, the fused features are decoded and classified by using the improved stacked self-encoder deep Q-network. The experimental results show that the proposed method outperforms existing methods on the dynamic equipment dataset of public and auxiliary systems.
关键词
Cross-modal attention mechanism,Multimodal information fusion,Deep Q network,Fault diagnosis; Public auxiliary system
报告人
Wenhua Zhang
chairman Guangdong Deer Smart Technology Co., Ltd稿件作者
全部评论
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
发表评论