Journal of Engineering Research

Industrial equipment operating under complex conditions often generates multi-source and multi-scale abnormal signals. A single sensor cannot comprehensively reflect the equipment's status, resulting in limitations in fault diagnosis accuracy and real-time performance. This paper proposes a fault diagnosis platform based on multimodal sensor fusion, which integrates vibration, acoustic, temperature, and current signals to achieve cross-modal feature extraction and collaborative analysis. By combining deep learning and machine learning algorithms, the platform extracts key features from high-dimensional data for intelligent classification and prediction. Experimental verification demonstrates that this platform enhances fault recognition accuracy and robustness under complex operating conditions while maintaining low latency, enabling efficient monitoring and intelligent management of industrial equipment health status.

Multimodal sensor; Fault diagnosis; Data fusion; Industrial equipment