Abstract Details
174 / 2025-06-11 19:29:11
Intelligent Fault Diagnosis of Dynamic Equipment in Public Auxiliary Systems Based on Multimodal Deep Q-Networks
Cross-modal attention mechanism,Multimodal information fusion,Deep Q network,Fault diagnosis; Public auxiliary system
Final Paper
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.
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
-
Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
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2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
