一个时间频域诊断网络用于ICE故障检测
Daijie Tang1, Zhiyong Yin1, Demu Wu1
1China Ship Scientific Research Center, Wuxi 214082, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
一个新的时间频域诊断网络 (TFDN) 通过结合时间和频率数据来增强内燃机 (ICE) 故障检测. 这种深度学习模型以较少的数据实现了高精度,使实时诊断成为可能.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 内部燃烧发动机 (ICE) 在实时故障诊断方面面临挑战,原因是传统方法对特征提取和数据要求的局限性.
- 现有的深度学习模型,如CNN和LSTM,难以有效地捕获时间和频率域特征,以进行全面的ICE故障分析.
研究的目的:
- 开发一个先进的深度学习模型,用于准确和高效的内燃机故障诊断.
- 解决现有方法在特征提取和数据依赖性方面的局限性,以实时监测ICE状态.
主要方法:
- 提出了一个新的时间频域诊断网络 (TFDN),集成并行时间域 (ResNet,自我注意) 和频域 (CNN) 功能提取路径.
- 利用Swish激活功能和批量正常化,以提高训练效率.
- 在一个包含12种不同的故障类型的六柴油发动机数据集上验证了TFDN模型.
主要成果:
- 在充满负载条件下,TFDN实现了高诊断精度 (98.12%~99.79%),超过了基线模型 (CNN,ResNet,LSTM).
- 该模型在混合操作条件下表现出强大的性能,保持高精度,精度和回忆.
- TFDN在有限的数据下显示出显著的稳定性,在每次故障仅有5个样本时,达到60%~70%的准确性.
结论:
- TFDN有效地整合了时间频率特征,大大提高了ICE的诊断准确性和稳定性.
- 拟议的网络提供了一种实用且数据效率高的解决方案,用于实时检测内燃机的故障和状态监测.
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