基于同步挤压S转换器和视觉转换器的柴油发动机故障状态识别方法的研究
Siyu Li1, Zichang Liu1, Yunbin Yan1
1Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang 050003, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种新的柴油发动机故障识别方法,使用同步挤压S变换器 (SSST) 和视觉变换器 (ViT). 该方法在检测柴油发动机故障方面实现了高精度,提高了可靠性和安全性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 柴油发动机的可靠性和安全性随着运行时间的推移而下降,导致频繁的故障.
- 传统的故障识别方法在精确的柴油发动机故障检测方面扎.
- 现有的信号时间频率分析方法显示出低分辨率和弱能量聚合.
研究的目的:
- 开发一种先进的柴油发动机故障状态识别方法.
- 克服传统方法在准确识别柴油发动机故障方面的局限性.
- 利用SSST在信号处理和ViT在图像分类方面的优势.
主要方法:
- 来自柴油发动机的振动信号使用传感器收集.
- 同步挤压S转换 (SSST) 将振动信号转换为2D时间频率图,增强分辨率和能量聚合.
- 视觉变压器 (ViT) 模型从这些时间频率图中提取特征以评估故障状态.
主要成果:
- 与ST-ViT,SSST-2DCNN和FT频谱-1DCNN相比,提出的SSST-ViT方法显示出更高的性能.
- 实现了高故障状态识别精度:98.31%在公共数据集上.
- 在实际实验室数据上获得的95.67%的卓越准确度.
结论:
- SSST-ViT方法为柴油发动机故障状态的识别提供了强大而准确的方法.
- 这种技术有效地解决了故障检测中非线性和非光滑信号处理的挑战.
- 该研究为加强柴油发动机状况监测和维护提供了一个有希望的新方向.
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