Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning

Yi Lu Murphey; Liping Huang; Hao Xing Wang; Yinghao Huang

doi:doi:10.11648/j.ijiis.20150403.12

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Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning

Yi Lu Murphey, Liping Huang, Hao Xing Wang, Yinghao Huang

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 3)

Received: 17 June 2015 Accepted: 29 June 2015 Published: 9 July 2015

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Abstract

This paper presents an intelligent vehicle fault diagnostics system, SeaProSel(Search-Prompt-Select). SeaProSel takes a casual description of vehicle problems as input and searches for a diagnostic code that accurately matches the problem description. SeaProSel was developed using automatic text classification and machine learning techniques combined with a prompt-and-select technique based on the vehicle diagnostic engineering structure to provide robust classification of the diagnostic code that accurately matches the problem description. Machine learning algorithms are developed to automatically learn words and terms, and their variations commonly used in verbal descriptions of vehicle problems, and to build a TCW(Term-Code-Weight) matrix that is used for measuring similarity between a document vector and a diagnostic code class vector. When no exactly matched diagnostic code is found based on the direct search using the TCW matrix, the SeaProSel system will search the vehicle fault diagnostic structure for the proper questions to pose to the user in order to obtain more details about the problem. A LSI (Latent Semantic Indexing) model is also presented and analyzed in the paper. The performances of the LSI model and TCW models are presented and discussed. An in-depth study of different term weight functions and their performances are presented. All experiments are conducted on real-world vehicle diagnostic data, and the results show that the proposed SeaProSel system generates accurate results efficiently for vehicle fault diagnostics.

Published in	International Journal of Intelligent Information Systems (Volume 4, Issue 3)
DOI	10.11648/j.ijiis.20150403.12
Page(s)	58-70
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Vehicle Fault Diagnostics, Text Data Mining, Machine Learning, Vehicle Diagnostic Engineering Structure, TCW, LSI

References

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APA Style

Yi Lu Murphey, Liping Huang, Hao Xing Wang, Yinghao Huang. (2015). Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning. International Journal of Intelligent Information Systems, 4(3), 58-70. https://doi.org/10.11648/j.ijiis.20150403.12

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ACS Style

Yi Lu Murphey; Liping Huang; Hao Xing Wang; Yinghao Huang. Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning. Int. J. Intell. Inf. Syst. 2015, 4(3), 58-70. doi: 10.11648/j.ijiis.20150403.12

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AMA Style

Yi Lu Murphey, Liping Huang, Hao Xing Wang, Yinghao Huang. Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning. Int J Intell Inf Syst. 2015;4(3):58-70. doi: 10.11648/j.ijiis.20150403.12

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@article{10.11648/j.ijiis.20150403.12,
  author = {Yi Lu Murphey and Liping Huang and Hao Xing Wang and Yinghao Huang},
  title = {Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {3},
  pages = {58-70},
  doi = {10.11648/j.ijiis.20150403.12},
  url = {https://doi.org/10.11648/j.ijiis.20150403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20150403.12},
  abstract = {This paper presents an intelligent vehicle fault diagnostics system, SeaProSel(Search-Prompt-Select). SeaProSel takes a casual description of vehicle problems as input and searches for a diagnostic code that accurately matches the problem description. SeaProSel was developed using automatic text classification and machine learning techniques combined with a prompt-and-select technique based on the vehicle diagnostic engineering structure to provide robust classification of the diagnostic code that accurately matches the problem description. Machine learning algorithms are developed to automatically learn words and terms, and their variations commonly used in verbal descriptions of vehicle problems, and to build a TCW(Term-Code-Weight) matrix that is used for measuring similarity between a document vector and a diagnostic code class vector. When no exactly matched diagnostic code is found based on the direct search using the TCW matrix, the SeaProSel system will search the vehicle fault diagnostic structure for the proper questions to pose to the user in order to obtain more details about the problem. A LSI (Latent Semantic Indexing) model is also presented and analyzed in the paper. The performances of the LSI model and TCW models are presented and discussed. An in-depth study of different term weight functions and their performances are presented. All experiments are conducted on real-world vehicle diagnostic data, and the results show that the proposed SeaProSel system generates accurate results efficiently for vehicle fault diagnostics.},
 year = {2015}
}

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TY  - JOUR
T1  - Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning
AU  - Yi Lu Murphey
AU  - Liping Huang
AU  - Hao Xing Wang
AU  - Yinghao Huang
Y1  - 2015/07/09
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.20150403.12
DO  - 10.11648/j.ijiis.20150403.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 58
EP  - 70
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20150403.12
AB  - This paper presents an intelligent vehicle fault diagnostics system, SeaProSel(Search-Prompt-Select). SeaProSel takes a casual description of vehicle problems as input and searches for a diagnostic code that accurately matches the problem description. SeaProSel was developed using automatic text classification and machine learning techniques combined with a prompt-and-select technique based on the vehicle diagnostic engineering structure to provide robust classification of the diagnostic code that accurately matches the problem description. Machine learning algorithms are developed to automatically learn words and terms, and their variations commonly used in verbal descriptions of vehicle problems, and to build a TCW(Term-Code-Weight) matrix that is used for measuring similarity between a document vector and a diagnostic code class vector. When no exactly matched diagnostic code is found based on the direct search using the TCW matrix, the SeaProSel system will search the vehicle fault diagnostic structure for the proper questions to pose to the user in order to obtain more details about the problem. A LSI (Latent Semantic Indexing) model is also presented and analyzed in the paper. The performances of the LSI model and TCW models are presented and discussed. An in-depth study of different term weight functions and their performances are presented. All experiments are conducted on real-world vehicle diagnostic data, and the results show that the proposed SeaProSel system generates accurate results efficiently for vehicle fault diagnostics.
VL  - 4
IS  - 3
ER  -

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Author Information

Yi Lu Murphey

Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, USA
Liping Huang

Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, USA
Hao Xing Wang

Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, USA
Yinghao Huang

Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, USA

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Plain Text BibTeX RIS

APA Style

Yi Lu Murphey, Liping Huang, Hao Xing Wang, Yinghao Huang. (2015). Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning. International Journal of Intelligent Information Systems, 4(3), 58-70. https://doi.org/10.11648/j.ijiis.20150403.12

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ACS Style

Yi Lu Murphey; Liping Huang; Hao Xing Wang; Yinghao Huang. Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning. Int. J. Intell. Inf. Syst. 2015, 4(3), 58-70. doi: 10.11648/j.ijiis.20150403.12

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AMA Style

Yi Lu Murphey, Liping Huang, Hao Xing Wang, Yinghao Huang. Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning. Int J Intell Inf Syst. 2015;4(3):58-70. doi: 10.11648/j.ijiis.20150403.12

Copy | Download

@article{10.11648/j.ijiis.20150403.12,
  author = {Yi Lu Murphey and Liping Huang and Hao Xing Wang and Yinghao Huang},
  title = {Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {3},
  pages = {58-70},
  doi = {10.11648/j.ijiis.20150403.12},
  url = {https://doi.org/10.11648/j.ijiis.20150403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20150403.12},
  abstract = {This paper presents an intelligent vehicle fault diagnostics system, SeaProSel(Search-Prompt-Select). SeaProSel takes a casual description of vehicle problems as input and searches for a diagnostic code that accurately matches the problem description. SeaProSel was developed using automatic text classification and machine learning techniques combined with a prompt-and-select technique based on the vehicle diagnostic engineering structure to provide robust classification of the diagnostic code that accurately matches the problem description. Machine learning algorithms are developed to automatically learn words and terms, and their variations commonly used in verbal descriptions of vehicle problems, and to build a TCW(Term-Code-Weight) matrix that is used for measuring similarity between a document vector and a diagnostic code class vector. When no exactly matched diagnostic code is found based on the direct search using the TCW matrix, the SeaProSel system will search the vehicle fault diagnostic structure for the proper questions to pose to the user in order to obtain more details about the problem. A LSI (Latent Semantic Indexing) model is also presented and analyzed in the paper. The performances of the LSI model and TCW models are presented and discussed. An in-depth study of different term weight functions and their performances are presented. All experiments are conducted on real-world vehicle diagnostic data, and the results show that the proposed SeaProSel system generates accurate results efficiently for vehicle fault diagnostics.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning
AU  - Yi Lu Murphey
AU  - Liping Huang
AU  - Hao Xing Wang
AU  - Yinghao Huang
Y1  - 2015/07/09
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.20150403.12
DO  - 10.11648/j.ijiis.20150403.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 58
EP  - 70
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20150403.12
AB  - This paper presents an intelligent vehicle fault diagnostics system, SeaProSel(Search-Prompt-Select). SeaProSel takes a casual description of vehicle problems as input and searches for a diagnostic code that accurately matches the problem description. SeaProSel was developed using automatic text classification and machine learning techniques combined with a prompt-and-select technique based on the vehicle diagnostic engineering structure to provide robust classification of the diagnostic code that accurately matches the problem description. Machine learning algorithms are developed to automatically learn words and terms, and their variations commonly used in verbal descriptions of vehicle problems, and to build a TCW(Term-Code-Weight) matrix that is used for measuring similarity between a document vector and a diagnostic code class vector. When no exactly matched diagnostic code is found based on the direct search using the TCW matrix, the SeaProSel system will search the vehicle fault diagnostic structure for the proper questions to pose to the user in order to obtain more details about the problem. A LSI (Latent Semantic Indexing) model is also presented and analyzed in the paper. The performances of the LSI model and TCW models are presented and discussed. An in-depth study of different term weight functions and their performances are presented. All experiments are conducted on real-world vehicle diagnostic data, and the results show that the proposed SeaProSel system generates accurate results efficiently for vehicle fault diagnostics.
VL  - 4
IS  - 3
ER  -

Copy | Download