Volume 4, Issue 3, June 2015, Page: 58-70
Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning
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
Received: Jun. 17, 2015;       Accepted: Jun. 29, 2015;       Published: Jul. 9, 2015
DOI: 10.11648/j.ijiis.20150403.12      View  3874      Downloads  116
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.
Vehicle Fault Diagnostics, Text Data Mining, Machine Learning, Vehicle Diagnostic Engineering Structure, TCW, LSI
To cite this article
Yi Lu Murphey, Liping Huang, Hao Xing Wang, Yinghao Huang, Vehicle Fault Diagnostics Using Text Mining, Vehicle Engineering Structure and Machine Learning, International Journal of Intelligent Information Systems. Vol. 4, No. 3, 2015, pp. 58-70. doi: 10.11648/j.ijiis.20150403.12
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