工业机器人中电机驱动控制系统的预测性维护和故障检测,使用基于CNN-RNN的观察员
1Department of Computer Science and Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了一种混合卷积神经网络-循环神经网络 (CNN-RNN) 模型,用于工业机器人直流电机驱动器的先进故障检测. 与现有方法相比,CNN-RNN模型提供了更快,更准确的预测性维护和故障诊断.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 工业机器人依靠直流电机驱动器进行精确的操作.
- 预测性维护对于最小化操作故障和确保系统寿命至关重要.
- 现有的故障检测方法往往缺乏实时应用所需的准确性和速度.
研究的目的:
- 开发和评估一种新的混合深度学习框架,用于增强直流电机驱动器的故障检测和预测性维护.
- 提高工业机器人电机系统故障预测的准确性和效率.
- 建立一个强大的AI模型,能够确定最佳的维护策略.
主要方法:
- 将卷积神经网络 (CNN) 和循环神经网络 (RNN) 集成到混合CNN-RNN模型中.
- 使用传感器数据 (例如温度,旋转速度) 进行培训和验证.
- 对CNN-LSTM,个别CNN,LSTM和传统方法进行比较分析.
主要成果:
- 与现有方法相比,拟议的CNN-RNN模型在故障预测和检测方面表现出卓越的准确性.
- 该CNN-RNN框架在故障诊断中实现了更高的精度.
- 该模型具有更简单的架构和更低的复杂性,导致比CNN-LSTM更快的处理速度.
结论:
- 混合CNN-RNN模型为工业机器人电机驱动器的实时故障检测提供了实用和高效的解决方案.
- 这种由人工智能驱动的方法提高了预测性维护,减少了运营停机时间.
- 该模型能够提取动态特征并处理顺序数据的能力确保了可靠的性能.
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