实时故障诊断在工业机器人中使用离散和斜线波波变换
Muhamad Azhar Abdilatef Alobaidy1, Jassim M Abdul-Jabbar2, Mohammed Aly3
1Mechatronics Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq.
Scientific reports
|October 1, 2025
概括
本研究介绍了一种实时故障诊断框架,用于使用离散波段转换 (DWT) 和斜线转换 (SLT) 的工业机器人. 该系统在检测多关节故障方面实现了100%的准确性,通过SLT.提供更快的检测时间.
科学领域:
- 机器人工程 机器人工程 机器人工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 工业机器人系统对于精密制造至关重要.
- 系统故障会导致大量的运行停机时间和可靠性降低.
- 实时故障诊断对于在动态的工业环境中保持性能至关重要.
研究的目的:
- 为多关节机器人手臂开发和评估基于硬件的实时故障诊断框架.
- 为了比较离散波形变换 (DWT) 和斜线变换 (SLT) 在故障检测方面的有效性.
- 使用现实世界的硬件实验验验证框架的性能.
主要方法:
- 利用来自LabVolt 5150机器人臂上的ADXL345传感器的加速数据.
- 应用离散波形变换 (DWT) 和斜线变换 (SLT) 进行特征提取.
- 使用多层感知器人工神经网络 (MLP-ANN) 进行故障分类.
主要成果:
- 在各种条件下使用DWT实现了多关节故障的100%分类准确性.
- 斜线转换 (SLT) 将故障检测延迟时间从7.8秒 (DWT) 降低到3.7秒.
- 通过硬件实验成功诊断了多个机器人关节的同时故障.
结论:
- 拟议的DWT和SLT集成框架为工业机器人提供准确和高效的实时故障诊断.
- 与DWT相比,SLT在减少检测延迟方面具有显著的优势.
- 这些发现为工业机器人故障检测中的算法选择提供了实际指导,并建议未来与基于图形的学习进行整合.
关键词:
分类 分类 分类 分类.这就是为什么DWT DWT DWT检测 检测 检测 检测 检测这是一个错误的错误.一个共同的联合行动.在MLP-NNN中使用.机器人手臂是一个机器人.在SLTT中,我们可以看到SLT.更多相关视频
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