流体机械2024，Vol.52Issue(7)：49-55,62,8.DOI:10.3969/j.issn.1005-0329.2024.07.007
融合贝叶斯网络与变权AHP的气量调控系统安全风险动态分析方法
Dynamic analysis method of safety risk of capacity control system based on Bayesian network and variable weight AHP
摘要
Abstract
In view of the problem that the stepless capacity control system of reciprocating compressor has a high failure rate and great impact on the operation of the unit,and the operation risk assessment methods of the existing system are difficult to complete the real-time and quantitative risk assessment,a risk dynamic analysis method for the safety of stepless capacity control system was proposed,which integrates Bayes network and variable weight AHP.The Bayesian network model,which includes fault type,fault mode and monitoring signal,was established to obtain the real-time probability of fault occurrence.Based on variable weight analytic hierarchy process(AHP),a semi-quantitative analysis model of fault hazard was established to calculate the influence index of fault mode.Further,the fault risk calculation formula of FMEA method was modified,the probability of fault occurrence was introduced,and the calculation method of historical fault and real-time operation risk was proposed.Test verification was carried out using the actual fault case data.The results show that the new method can quantify the real-time and historical operating risks of computer units,and the normalized real-time operating risk threshold is set at 0.5 to determine the need for maintenance.The research results can provide quantitative indexes for the development of inspection and maintenance plans for reciprocating compressors and gas volume regulation systems.关键词
无级气量调控系统/贝叶斯网络/故障模式与影响分析/层次分析法/风险动态分析Key words
stepless capacity control system/Bayesian network/fault mode and effect analysis/analytic hierarchy process/risk dynamic analysis分类
机械制造引用本文复制引用
董良遇,张哲宇,张进杰,王瑶..融合贝叶斯网络与变权AHP的气量调控系统安全风险动态分析方法[J].流体机械,2024,52(7):49-55,62,8.基金项目
重庆市技术创新与应用发展专项面上项目(cstc2020jscx-msxm0411) （cstc2020jscx-msxm0411）
中央高校基本科研业务费资助项目(XJ2023000901) （XJ2023000901）
