机器学习用于检测和诊断由电机驱动的应用程序中的异常
Fábio Ferraz Júnior1, Roseli Aparecida Francelin Romero2, Sheng-Jen Hsieh3
1Mechatronic Engineering, INSPER-Institute of Education and Research, São Paulo 04546-042, SP, Brazil.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
本研究提出了一种方法来选择电机驱动的旋转机器预测性维护 (PdM) 的关键变量. 机器学习算法有效地分类异常,k-最近邻居和多层感知子显示最高准确度.
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:
- 现代制造系统需要跨价值网络的灵活性和整合.
- 从车间设备获取数据为战略决策提供了洞察力.
- 尽管有潜力,预测性维护 (PdM) 在行业中的采用仍然很低.
研究的目的:
- 提出一种方法来选择电机驱动的旋转机器仪表的关键属性.
- 减少与PdM定义变量相关的成本和时间.
- 为此选择任务确定合适的数据科学和机器学习算法.
主要方法:
- 调查众所周知的数据科学和机器学习算法.
- 开发测试场景以检测各种异常 (未合,过载,不平衡,不对齐,正常).
- 对算法在分类不同类型异常中的性能进行评估.
主要成果:
- 机器学习算法在分类旋转机械中的异常方面表现出有效性.
- K-近邻 (KNN) 和多层感知子 (MLP) 模型实现了最高的精度.
- 拟议的方法有助于优化PdM的仪器设置.
结论:
- 数据驱动的方法,特别是机器学习,对于推进制造业的PdM至关重要.
- 优化的属性选择提高了PdM实施的效率和成本效益.
- 在工业旋转机械中,KNN和MLP显示出异常检测和分类的前景.
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