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Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

机译：基于文本案例的推理框架，用于云计算的故障诊断和预测

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摘要

In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the BigData records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.
机译：在诸如铁路车载系统的离散事件系统中，大量的文本数据以在故障诊断过程中收集的逐字记录的形式记录下来。对此类维护数据进行有效的文本挖掘对于从数百万个修复细节中发现最佳实践的修复知识起着重要作用，这有助于进行准确的故障诊断和预测。本文提出了一种基于文本案例的云计算推理框架，该框架使用诊断本体对逐字记录的故障特征进行注释。提取的故障特征通过粗糙集理论进一步减少。最后，利用案例检索来搜索修复故障零件的最佳实践维修措施。通过云计算，基于粗糙集的属性约简和案例检索能够扩展BigData记录并提高故障诊断和预测的效率。通过对火车车载设备的故障诊断，验证了该方法的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第8期|9464971.1-9464971.10|共10页
  • 作者

    Zhong Zhiwang; Xu Tianhua; Wang Feng; Tang Tao;

  • 作者单位

    Beijing Jiaotong Univ, State Key Lab Traff Control & Safety, Beijing 100044, Peoples R China;

    Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China;

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