[1]陈孟婷,付晓薇*,樊 洋,等.基于因果分解的 SOFC 系统故障根源诊断方法[J].计算机技术与发展,2021,31(10):168-172.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 028]
 CHEN Meng-ting,FU Xiao-wei*,FAN Yang,et al.Root Cause Diagnosis of SOFC System Based on Causality Decomposition[J].,2021,31(10):168-172.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 028]
点击复制

基于因果分解的 SOFC 系统故障根源诊断方法()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年10期
页码:
168-172
栏目:
应用前沿与综合
出版日期:
2021-10-10

文章信息/Info

Title:
Root Cause Diagnosis of SOFC System Based on Causality Decomposition
文章编号:
1673-629X(2021)10-0168-05
作者:
陈孟婷12 付晓薇12* 樊 洋12 李 曦3
1. 武汉科技大学 计算机科学与技术学院,湖北 武汉 430065;
2. 智能信息处理与实时工业系统湖北省重点实验室,湖北 武汉 430065;
3. 华中科技大学 人工智能与自动化学院,湖北 武汉 430074
Author(s):
CHEN Meng-ting12 FU Xiao-wei12* FAN Yang12 LI Xi3
1. School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;
2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan 430065,China;
3. School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China
关键词:
固体氧化物燃料电池数据驱动故障根源诊断集成经验模式分解瞬时相位相关性
Keywords:
solid oxide fuel cell ( SOFC) data - driven root cause diagnosis ensemble empirical mode decomposition ( EEMD ) instantaneous phase correlation
分类号:
TP206+. 3
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 10. 028
摘要:
固体氧化物燃料电池(solid oxide fuel cell,SOFC) 具有高效率、低排放、长寿命运行等特点。 SOFC 系统发电过程中存在时变、非线性和非平稳性等现象,会引起性能演化衰变,进而导致不明故障而停机。 鉴于此,文中提出了一种基于因果分解的故障根源诊断方法。该方法运用集成经验模式分解 ( EEMD)方法将过程数据分解为有限个固有模式函数( IMF)以表征原始信号在不同时间尺度上的局部特征,同时引入瞬时相位相关性概念以定量评估 IMF 分量间因果作用,从而达到因果分解的目的。 此外,该方法在考虑时间优先原则的同时,强调因果交互作用的瞬时关系,因而增强双向因果关系检测与分析能力。 实验结果表明,该方法通过可靠的故障传播路径分析可实现故障源准确定位,为故障演化机理分析提供支撑,具有较强实用性。
Abstract:
Solid oxide fuel cell ( SOFC) has the characteristics of high efficiency, low emission and long lifetime. There are time-varying,nonlinear and non- stationary phenomena in the generation process of SOFC systems, which may cause the degradation of performance evolution and in turn leads to the shutdown of unknown faults. In view of this,we propose a root cause diagnosis method based on causality decomposition. In this method, the ensemble empirical mode decomposition ( EEMD) is used to decompose the process data into a limited number of intrinsic mode function? ? ? ?( IMF) to characterize the local characteristics of the original signal at different time scales. At the same time,the concept of instantaneous phase correlation is introduced to quantitatively evaluate the causal effect between the IMF components. The experiment shows that the proposed method can accurately locate the fault source through reliable fault propagation path analysis. It provides support for fault evolution mechanism analysis and has strong practicability.

相似文献/References:

[1]朱菊 王志坚 杨雪.基于数据驱动的软件自动化测试框架[J].计算机技术与发展,2006,(05):68.
 ZHU Ju,WANG Zhi-jian,YANG Xue.A Software Automation Test Frameworks Based on Data- Driven Automation Methodology[J].,2006,(10):68.
[2]梁宗保,胡怡然,张凯.桥梁健康监测信息的数据驱动处理方法研究[J].计算机技术与发展,2013,(10):258.
 LIANG Zong-bao[],HU Yi-ran[],ZHANG Kai[].Research of Data Drive Processing Method of Bridge Health Monitoring Information[J].,2013,(10):258.
[3]黄承宁,李 娟,陈嘉政.基于图神经网络的医疗物资智能调度研究优化[J].计算机技术与发展,2021,31(09):202.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 034]
 HUANG Cheng-ning,LI Juan,CHEN Jia-zheng.Research and Optimization of Medical Material Intelligent Scheduling Based on Graph Neural Network[J].,2021,31(10):202.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 034]
[4]张文龙,张 洁.基于 A3C 的有序充电算法[J].计算机技术与发展,2023,33(01):173.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 026]
 ZHANG Wen-long,ZHANG Jie.Orderly Charging Algorithm Based on A3C[J].,2023,33(10):173.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 026]

更新日期/Last Update: 2021-10-10