一个基于机器人操作系统的智能机器盒及其对预测性维护的应用
Yeong-Hwa Chang1,2, Yu-Hsiang Chai1, Bo-Lin Li1
1Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了用于预测性维护的智能机箱,使设备能够进行主动维护. 该系统有效地使用数据分析和机器学习模型预测剩余的使用寿命.
科学领域:
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 预测性维护提供了一个主动的策略,而不是传统的计划或反应性维护.
- 工业机械产生必要的数据,用于状态识别和维护规划.
- 机箱用于收集和传输制造机器数据.
研究的目的:
- 设计和实施智能机器盒,用于工业环境中增强数据收集.
- 集成各种传感器模块的各种通信接口.
- 为有效的预测性维护应用利用收集的数据.
主要方法:
- 使用机器人操作系统 (ROS) 框架开发机器箱.
- 结合多个通信接口来获取传感器数据.
- 在数据分析中应用相关性分析来选择特征.
- 实现机器学习模型,包括线性回归,神经网络和预测建模的决策树.
主要成果:
- 通过实验验证证明拟议的智能机器箱的可行性.
- 成功预测机器的剩余使用寿命 (RUL).
- 通过相关性分析识别主导特征,以提高模型准确性.
结论:
- 开发的智能机箱有效支持预测性维护计划.
- 该系统能够准确预测设备的剩余使用寿命.
- 这种方法促进了主动的维护计划,减少了停机时间和运营成本.
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