Bilevel Feature Extraction-Based Text Mining for Fault Diagnosis of Railway Systems

Wang, Feng; Xu, Tianhua; Tang, Tao; Zhou, MengChu; Wang, Haifeng

doi:10.1109/tits.2016.2521866

Cited by 116 publications

(53 citation statements)

References 23 publications

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“…In railway industry, millions of such repair verbatims are generated every year. Table 1 [7] gives a simple example with two verbatims. They provide useful data from which the knowledge must be discovered for efficient fault diagnosis and handling of the similar cases in the future.…”

Section: Data Acquisition and Feature Extractionmentioning

confidence: 99%

“…Rajpathak et al [6] also present a real-life reliability system by fusing the field warranty failure data with the failure modes extracted from unstructured repair verbatim data by using the ontology based natural language processing technique to facilitate accurate estimation of component reliability. Wang and Xu et al [7] present a bilevel feature extraction-based text mining that integrates features extracted at both syntax and semantic levels with the aim of improving the fault classification performance for railway onboard equipment, considering the fact that, in a maintenance situation, the operators always search solution of fault diagnosis and predication problems that could be very similar to other states, which have been previously processed. In these cases, the corresponding fault diagnosis and predication solutions are expected to be correlated to these similar system states.…”

Section: Introductionmentioning

confidence: 99%

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

Zhong

Wang

et al. 2018

Mathematical Problems in Engineering

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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.

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Section: Data Acquisition and Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhong

Wang

et al. 2018

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors in [39] detailed a novel approach to solve the assignment problem of finding optimum thresholds for axle-based vehicle classifiers. The authors in [40] proposed a bilevel feature extraction-based text mining that integrates features extracted at both syntax and semantic levels with the aim to improve the fault classification performance.…”

Section: Related Workmentioning

confidence: 99%

Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models

et al. 2017

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To address the issue of fine-grained classification of Internet multimedia traffic from a Quality of Service (QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition (K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bag-QoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoS-words, Locality Constrained Feature Coding (LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines (SVM) classifier. Our experimental results demonstrate the feasibility of the proposed classification method.

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“…The granulation model of universe space is formed by the neighborhood of any object in the universe space and realizes the granular computing of continuous numerical space. The work [29] enhances the precision of fault diagnosis for all fault classes by using Chi-square statistics to reduce attribute dimensions. A cost-sensitive embedded feature selection method is proposed to solve the class imbalance problem in [30].…”

Section: Introductionmentioning

confidence: 99%

Incremental Attribute Reduction Method Based on Chi-Square Statistics and Information Entropy

Yan

et al. 2020

IEEE Access

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Attributes in datasets are usually not equally significant. Some attributes are unnecessary or redundant. Attribute reduction is an important research issue of rough set theory, which can find minimum subsets of attributes with the same classification effect as the whole dataset by removing unnecessary or redundant attributes. We use Chi-square statistics to evaluate the significance of condition attributes. It can reduce the search space of attribute reduction and improve the speed of attribute reduction. Conditional entropy of relative attributes is adopted as a heuristic function. Two decision table reduction algorithms, forward selection and backward deletion, are proposed to approach the optimal solution. Based on this, an efficient incremental attribute reduction method for dynamically changing datasets is proposed by preserving intermediate variables. The intermediate variable is the observation frequency matrix of joint events of each condition attribute and decision attribute. Experimental results show that the proposed algorithms can improve performance in terms of processing time.

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Bilevel Feature Extraction-Based Text Mining for Fault Diagnosis of Railway Systems

Cited by 116 publications

References 23 publications

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

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

Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models

Incremental Attribute Reduction Method Based on Chi-Square Statistics and Information Entropy

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