An ontology based text mining system for knowledge discovery from the diagnosis data in the automotive domain

Rajpathak, Dnyanesh

doi:10.1016/j.compind.2013.03.001

Cited by 65 publications

(32 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A small window size has an advantage of requiring less system space and running time [30]. Here, we use a small window size of 4 to conduct the synonym term identification, which is similar to [31]. In order to extract features in a window size of 4, a document annotation by ontology is needed.…”

Section: Data Acquisition and Feature Extractionmentioning

confidence: 99%

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

Zhong

Wang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Data Acquisition and Feature Extractionmentioning

confidence: 99%

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

Zhong

Wang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The on-board fault diagnostics and prognostics systems are responsible for continuously monitoring the critical operation of each system. In case of a malfunction, the system generates a Diagnostic Trouble Code (DTC) which is then stored within the on-board computer of a vehicle [23]. DTCs generated over a period of time can be extracted by dealers using the appropriate software tool.…”

Section: On-board Fault Diagnostics and Prognosticsmentioning

confidence: 99%

“…Chougule et al [24] developed a novel approach for integrating warranty claims and vehicle diagnostics to support engineers in identifying anomalies in the field, performing root cause analysis and capturing training needs for dealers. Rajpathak [23] proposes a novel ontology-based text mining system in order to analyse unstructured textual diagnosis data collected from automotive OEMs during the warranty period. Saxena et al [29] propose a hybrid reasoning architecture for integrated fault diagnosis and health maintenance of fleet vehicles to support maintenance decisionmaking and capture knowledge.…”

Section: On-board Fault Diagnostics and Prognosticsmentioning

confidence: 99%

Improving root cause analysis through the integration of PLM systems with cross supply chain maintenance data

Madenas

Tiwari

Turner

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

A B S T R A C TThe purpose of this paper is to demonstrate a system architecture for integrating Product Lifecycle Management (PLM) systems with cross supply chain maintenance information to support root-cause analysis. By integrating product-data from PLM systems with warranty claims, vehicle diagnostics and technical publications, engineers were able to improve the root-cause analysis and close the information gaps. Data collection was achieved via in-depth semi-structured interviews and workshops with experts from the automotive sector. Unified Modelling Language (UML) diagrams were used to design the system architecture proposed. A user scenario is also presented to demonstrate the functionality of the system.

show abstract

“…Text mining applications are ubiquitous, spanning a multitude of industries, including construction [7], automotive [8], and process [9] industries. Text mining has also widely been applied to renewable energy, including wind energy, and it has also been utilized for accident data analysis.…”

Section: Literaturementioning

confidence: 99%

“…Descriptive ontology is concerned with the collection of information, whereas formal ontology distills, filters, codifies and organizes the results of descriptive ontology. While ontology is a scientific discipline, an ontology is a classification of categories; a formal, explicit specification of shared conceptualization 31], pages [8][9].…”

Section: E Ontology Developmentmentioning

confidence: 99%

Text mining analysis of wind turbine accidents: An ontology-based framework

Ertek

Zhang

2017

2017 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

Abstract-As the global energy demand is increasing, the share of renewable energy and specifically wind energy in the supply is growing. While vast literature exists on the design and operation of wind turbines, there exists a gap in the literature with regards to the investigation and analysis of wind turbine accidents. This paper describes the application of text mining and machine learning techniques for discovering actionable insights and knowledge from news articles on wind turbine accidents. The applied analysis methods are text processing, clustering, and multidimensional scaling (MDS). These methods have been combined under a single analysis framework, and new insights have been discovered for the domain. The results of our research can be used by wind turbine manufacturers, engineering companies, insurance companies, and government institutions to address problem areas and enhance systems and processes throughout the wind energy value chain.

show abstract

An ontology based text mining system for knowledge discovery from the diagnosis data in the automotive domain

Cited by 65 publications

References 27 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

Improving root cause analysis through the integration of PLM systems with cross supply chain maintenance data

Text mining analysis of wind turbine accidents: An ontology-based framework

Contact Info

Product

Resources

About