Abstract
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 Big Data 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.
Highlights
Discrete Event System (DES), such as railway onboard system, produces a large amount of text data by recording the maintenance process, which consists of symptoms corresponding to faulty parts, observed failure modes, and repair actions taken to fix the faults
The main idea is to extract fault features by text mining, reduce attributes by rough set theory [20,21,22,23,24,25], and solve the fault diagnosis and predication problem by deploying a CaseBased Reasoning (CBR) module based on the Hadoop platform with MapReduce framework [26, 27], which is a computing paradigm for Big Data management created at Google
This paper presents a text case-based reasoning framework for fault diagnosis and predication by cloud computing, which integrates the text mining, rough-based attributes reduction, and case retrieval by cloud computing
Summary
Discrete Event System (DES), such as railway onboard system, produces a large amount of text data by recording the maintenance process, which consists of symptoms corresponding to faulty parts, observed failure modes, and repair actions taken to fix the faults. The main idea is to extract fault features by text mining, reduce attributes by rough set theory [20,21,22,23,24,25], and solve the fault diagnosis and predication problem by deploying a CBR module based on the Hadoop platform with MapReduce framework [26, 27], which is a computing paradigm for Big Data management created at Google. This computing paradigm is able to scale up the computing to thousands of processors and terabytes (or petabytes) of data.
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