基于动态视觉的非接触旋转机器故障诊断使用EViT
Zhenning Jin1, Cuiying Sun2, Xiang Li1
1Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
基于事件的摄像头提供先进的机器振动传感器,用于故障诊断. 一个新视觉变压器 (EViT) 处理复杂的动态视觉数据,在工业环境中实现精确的非接触式诊断.
科学领域:
- 机器人和自动化 机器人和自动化
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 基于事件的摄像头提供高时间分辨率和动态范围,用于捕捉机械现象.
- 传统的成像系统难以处理来自这些传感器的复杂动态视觉数据.
- 机器振动传感和故障诊断需要先进的非接触式方法.
研究的目的:
- 利用动态视觉数据开发一种非接触式机器故障诊断方法.
- 引入一个有效的计算框架来处理异步事件流.
- 提高智能制造环境中的预测性维护.
主要方法:
- 提出了视力变压器 (EViT) 架构与双自动注意和双前网络.
- 开发了一个计算框架来处理异步事件流,同时保持时间精度.
- 利用了多尺度的时空特征分析和自适应性学习.
主要成果:
- 在各种操作场景中表现出卓越的故障诊断性能.
- 从基于事件的摄像头成功处理了复杂的动态视觉数据.
- 使用旋转机器的实际状态监测数据验证了该方法.
结论:
- 拟议的EViT方法为非接触式机械故障诊断提供了一个新的范式.
- 这种方法有效地将生物启发的视觉处理与工业监控需求相结合.
- 这些发现为预测性维护应用提供了宝贵的见解.
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