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相关概念视频

Frictional Force01:07

Frictional Force

7.4K
When a body is in motion, it encounters resistance because the body interacts with its surroundings. This resistance is known as friction, a common yet complex force whose behavior is still not completely understood. Friction opposes relative motion between systems in contact, but also allows us to move. Friction arises in part due to the roughness of surfaces in contact. For one object to move along a surface, it must rise to where the peaks of the surface can skip along the bottom of the...
7.4K
Machines: Problem Solving I01:22

Machines: Problem Solving I

294
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
294

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相关实验视频

Updated: Jun 23, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

深度学习工具 基于切断力信号的磨损状态识别方法

Shuhang Li1, Meiqiu Li1, Yingning Gao1

  • 1School of Mechanical Engineering, Yangtze University, Jingzhou 434023, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括

本研究介绍了一种改进的MobileViT网络,用于精确识别削刀具磨损状态. 该方法提高了实时工业应用的准确性和效率.

科学领域:

  • 制造业 工程 制造工程
  • 人工智能的人工智能
  • 信号处理 信号处理

背景情况:

  • 精确识别削刀具磨损对于优化加工流程和防止工具故障至关重要.
  • 现有的方法可能缺乏实时工业监控所需的效率和准确性.

研究的目的:

  • 开发一种准确,快速和高效的方法来识别削刀具的磨损状态.
  • 提高用于工具磨损监控的轻量级神经网络的性能.

主要方法:

  • 提出了一种结合连续波形变换 (CWT) 和改进的MobileViT轻量级网络的新方法.
  • CWT将切割力信号转化为时间频率图像.
  • 移动ViT网络增强了一个上下文变压器模块和一个全球注意力机制模块.

主要成果:

  • 与原来的MobileViT相比,增强的MobileViT模型在准确性 (1.58%),精度 (1.23%),回忆 (1.92%) 和F1得分 (1.57%) 中显示出显著的改进.
  • 拟议的模型在内存占用和预测准确度方面比VGG16,ResNet18和Pool Former有优势.
  • 该方法可以近乎实时识别工具磨损状态.

结论:

关键词:
移动ViT 移动ViT 在线语境变压器是一个语境变压器.连续波形变换连续波形变换.全球关注机制全球关注机制研磨机切割器状态识别识别

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Last Updated: Jun 23, 2026

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  • 拟议的方法提供了一种高效和准确的方法,用于削刀具磨损状态的识别.
  • 增强的MobileViT网络为工具磨损监控提供了卓越的性能.
  • 这种技术在工业生产环境中具有很强的应用潜力.