设计和实施用于工业机械的外国直接投资模糊分类器
Silvia Maria Zanoli1, Crescenzo Pepe1
1Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
一个新的故障检测和隔离 (FDI) 系统提高了工业机械的可靠性. 该系统使用主要组件分析 (PCA),集群分析和模式识别来有效监测和维护故障.
科学领域:
- 工业工程 工业工程 工业工程
- 机械工程 机械工程
- 控制系统 控制系统
背景情况:
- 工业机械,特别是合成气制造中的多轴离心压缩机,容易发生各种故障.
- 仪器和工艺故障,如污染和轴承故障,可能导致重大运行问题和安全问题.
研究的目的:
- 为工业机械设计和实施先进的故障检测和隔离 (FDI) 系统.
- 监测和诊断多轴离心压缩机的故障,包括仪器和过程相关的问题.
主要方法:
- 开发了一种结合主要组件分析 (PCA),集群分析和模式识别的新方法.
- 用ANOVA (VAriance的分析) 来进行最佳的主要组件 (PC) 选择.
- 模糊逻辑在模糊故障分类器模块中用于故障诊断.
主要成果:
- 该系统有效地检测和隔离单个和多个仪器故障,以及工艺故障,如污染和推力轴承故障.
- 在真实工厂数据上的实验结果验证了拟议的外国直接投资方法.
- 该系统展示了通过数据聚类来处理过程故障分析中的复杂性增长的能力.
结论:
- 开发的FDI系统显著改善了维护操作,提高了压缩机的可靠性和可用性.
- 该系统有助于提高工厂安全性和降低运营成本.
- 集成PCA,集群分析和模式识别为复杂的工业故障诊断提供了强大的解决方案.
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