基于风险的故障检测使用基于故障模式和效果分析的贝叶斯网络
Bálint Levente Tarcsay1, Ágnes Bárkányi1, Sándor Németh1
1Department of Process Engineering, University of Pannonia, 8200 Veszprém, Hungary.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了一种混合故障检测 (FD) 方法,使用动态主要组件分析 (DPCA) 和故障模式和效应分析 (FMEA) 的贝叶斯网络 (BNs). 这种方法通过评估工艺故障风险来提高工业安全,以尽量减少错误报警.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程安全 过程安全 过程安全
- 工业监控 工业监控 工业监控
背景情况:
- 工业故障检测 (FD) 方法往往缺乏对过程风险评估的强有力的整合.
- 尽量减少报警率需要区分安全关键和非安全关键的过程异常.
- 现有的FD技术为整合动态风险分析提供了有限的能力.
研究的目的:
- 引入一种新的基于风险的混合故障检测 (FD) 方法.
- 将动态主要组件分析 (DPCA) 与基于贝叶斯网络 (BNs) 的故障模式和影响分析 (FMEA) 整合起来.
- 通过评估过程故障风险和最大限度地降低报警率来提高故障检测的准确性.
主要方法:
- 开发了一个混合模型,结合了DPCA和FMEA的BN.
- 利用FMEA构建一个为监督过程的BN.
- 雇佣DPCA分析过程数据并估计修改后的风险优先级号码 (RPN).
主要成果:
- 拟议的混合方法有效地估计了不同过程状态的修改RPN.
- 通过结合BN和DPCA结果,成功地区分了过程异常.
- 在工业基准和液态有机载 (LOHC) 反应堆模型上证明了该方法的有效性.
结论:
- 混合DPCA-FMEA-BN方法为基于风险的故障检测提供了一个强大的框架.
- 这种方法通过准确识别关键过程故障来提高工业安全.
- 该技术适用于复杂的工业过程,包括像LOHC这样的新兴技术.
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