汽车动力智能故障诊断模型构建及检验

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Abstract

汽车动力系统故障诊断是保障行车安全、提升运维效率的重中之重，本文聚焦智能故障诊断模型的理论构建及验证，探讨基于深度学习的多模态信号融合分析框架。通过构建包含信号感知层、特征融合层、智能诊断层及决策输出层的理论模型，并采用蒙特卡洛交叉验证与迁移学习理论模拟，证明了其在噪声干扰与工况迁移下的强泛化性与鲁棒性，以期为汽车动力系统智能化运维提供了坚实的理论支撑与方法论指导。

Keywords

动力系统

故障诊断

深度学习

特征融合

图卷积网络

References

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