压缩机故障诊断和结果可视化基于视觉变压器的融合和改进的剩余网络
Xianling Duan1, Shaolin Hu2, Sijing Wang3
1School of Information and Control Engineering Jilin Institute of Chemical Technology, Jilin, Jilin, 132000, China.
Heliyon
|September 16, 2024
概括
这项研究引入了一种新的深度残留收缩视觉网络 (DRS-ViT),用于精确地诊断石油化工行业压缩机故障,在识别设备故障时达到99.5%的准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 石化工程是石油化学工程的重要组成部分.
背景情况:
- 压缩机故障诊断对于石化生产的安全性和效率至关重要.
- 传统方法往往缺乏复杂工业环境所需的准确性和及时性.
研究的目的:
- 为及时检测和诊断压缩机故障开发一个高度准确和强大的深度学习模型.
- 利用先进的深度学习架构来增强功能提取和压缩机操作数据的分类.
主要方法:
- 构建了一个深度残留收缩视觉网络 (DRS-ViT),集成了一个修改后的残留网络 (ResNet) 和视觉变压器 (ViT).
- 将压缩机振动信号转换为使用格拉米安角总和场 (GAF) 的格拉米安角总和场 (GASF) 图像.
- 使用ResNet进行初始特征提取,然后使用ViT进行故障分类.
主要成果:
- DRS-ViT模型实现了99.5%的故障诊断准确度.
- 模型可视化证实了其有效识别特定故障点的能力.
- 对比分析验证了该模型的优越有效性和稳定性与其他方法相比.
结论:
- DRS-ViT模型为石化行业的压缩机故障诊断提供了一个高度准确和可靠的解决方案.
- 将GASF成像与DRS-ViT集成展示了复杂机械健康监测的有希望的方法.
- 这些发现支持采用先进的人工智能技术来改善工业设备的维护和运营完整性.
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