设计和实现压缩机数据异常安全监测和诱导可追溯性专家系统的设计和实现
1School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin, Jilin, China.
PloS one
|January 17, 2025
概括
这项研究引入了一种用于监测离心压缩机数据异常并追踪它们在石油和天然气行业的根本原因的新方法. 这种方法有效地减少了错误报警,提高了故障诊断的准确性.
科学领域:
- 石化工程是石油化学工程的重要组成部分.
- 工业安全 工业安全 工业安全
- 数据科学数据科学数据科学
背景情况:
- 离心压缩机在石油和天然气行业中至关重要.
- 准确的故障诊断和根本原因追踪对于运营完整性至关重要.
- 现有的固定值报警方法存在错误和错过的报警.
研究的目的:
- 为压缩机数据异常开发安全监控和根源追踪方法.
- 设计一个智能系统,用于压缩机的故障追踪和定位.
- 提高工业环境中故障检测和诊断的可靠性.
主要方法:
- 一个故障动态知识图的构建.
- 仪表数据的滑动耐故障过.
- 动态知识图的融合和推理与仪器数据变化监测.
- 开发一个主要组件分析 (PCA) - 平方预测错误 (SPE) - 卷积神经网络 (CNN) 分类器.
- 与自然语言处理 (NLP) 集成用于推断.
主要成果:
- 提出的方法有效地克服了固定值警报的局限性,减少了错误和错过的警报.
- 在"驱动机不启动"和"低油压"故障中实现了100%的分类准确性.
- 通过动态知识图和NLP推断的联合使用,证明了良好的诊断结果.
结论:
- 开发的方法为监测和诊断离心压缩机故障提供了强大的解决方案.
- 大数据,知识图和先进机器学习的整合增强了故障追踪能力.
- 这种方法显著提高了石油化工行业压缩机操作的安全性和可靠性.
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