Industrial equipment fault diagnosis platform with multimodal sensor fusion
多模态传感器融合的工业设备故障诊断平台

摘要

工业设备在复杂工况下运行时容易产生多源、多尺度的异常信号，单一传感器难以全面反映设备状态，导致故障诊断精度和实时性受限。本文提出基于多模态传感器融合的故障诊断平台，通过整合振动、声学、温度、电流等多类信号，实现跨模态特征提取与信息协同分析。结合深度学习和机器学习算法，平台能够从高维数据中提取关键特征并进行智能分类与预测。实验验证表明，该平台在复杂工况下提高了故障识别准确率和鲁棒性，同时保持较低延迟，实现了工业设备健康状态的高效监测与智能化管理。

Abstract

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.