A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis cell condition
A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis cell condition
复制标题
用于铝电解槽异常状况根本原因分析的知识推理模糊贝叶斯网络
DOI：
10.1007/s11705-017-1663-x
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发表时间：
2017-08
影响因子：
4.5
通讯作者：
Gui Weihua
中科院分区：
文献类型：
--
作者：
Yue Weichao;Chen Xiaofang;Gui Weihua
铝电解槽异常状态的根本原因分析（RCA）由于其基于多源知识的分析固有的复杂性而长期以来一直是具有挑战性的工业问题。另外，准确的异常状态RCA是提高电流效率的前提。异常工况RCA是一项多源知识融合的复杂工作，由于经验丰富的技术人员数量减少且流动频繁，难以保证异常工况RCA的准确性。鉴于此，提出了一种基于模糊贝叶斯网络的多源知识固化推理模型的构建方法。该方法能有效地融合和固化技术人员分析异常原因的知识，为复杂的故障诊断任务提供清晰直观的框架，并自动实现故障根源分析。在20组异常单元条件下对该方法进行了验证，并通过贝叶斯诊断推理找到具有最大后验概率的根节点异常状态，实现了根本原因分析。测试结果的正确率达到95%以上，表明了铝电解槽RCA知识推理的可行性。
Root cause analysis (RCA) of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy-Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.
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影响因子：
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作者：
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影响因子：
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