工具磨损状况监测方法基于深度学习,使用力信号
Yaping Zhang1,2, Xiaozhi Qi1, Tao Wang2
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种用于工具磨损监控的新深度学习模型. 输入到卷积神经网络 (CNN) 的连续波形转换 (CWT) 图像在识别工具磨损状态方面取得了超过90%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
- 信号处理 信号处理
背景情况:
- 准确的工具磨损监测对于自动化机械加工至关重要,提高了质量和效率.
- 现有的方法需要改进,以可靠地识别工具磨损状态.
研究的目的:
- 开发和评估一种新的深度学习模型,用于精确监测工具磨损状况.
- 为了研究将力信号转化为用于磨损状态识别的二维图像的有效性.
主要方法:
- 使用连续波形变换 (CWT),短时间里埃变换 (STFT) 和格拉米安角总和场 (GASF),将力信号转换为2D图像.
- 使用这些生成的图像开发和训练了一个卷积神经网络 (CNN) 模型.
- 性能与已经建立的模型 (如AlexNet和ResNet.Net) 相比较.
主要成果:
- 拟议的CNN模型在工具磨损状态识别方面实现了90%以上的准确性.
- 通过CWT生成的图像,当由CNN处理时,产生了最高的准确性.
- 由于CWT能够提取局部特征并抵抗噪声,这有助于其卓越的性能.
结论:
- 将力信号转换为2D图像是工具磨损状态识别的可行策略.
- 开发的CNN模型,特别是使用CWT生成的图像,显示了工业应用的巨大潜力.
- 这种方法在自动化机械加工和条件监测方面提供了有前途的进步.
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