A Novel CBR-Decision Tree Based Intelligent Car Fault Diagnosis System (CFDS)
Sumana De1, Baisakhi Chakraborty2 

1Sumana De is with the Department of Computer Science & Engineering, National Institute of Technology, Durgapur, West Bengal, India
2Baisakhi Chakraborty is with the Department of Computer Science & Engineering, National Institute of Technology, Durgapur, West Bengal, India

Manuscript received on 19 March 2019 | Revised Manuscript received on 24 March 2019 | Manuscript published on 30 July 2019 | PP: 2180-2194 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2390078219/19©BEIESP | DOI: 10.35940/ijrte.B2390.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Fault diagnostic systems find numerous applications in almost all service domains. Now a days, interest lies on intelligent fault diagnosis. For this, a huge database of cases called Case Base (CB) comprising of fault descriptions and their solutions needs to be maintained. Case Based Reasoning (CBR) is a popular Artificial Intelligence technique that supports huge databases that find popular applications in fault diagnosis systems. CBR is a very useful process for solving problems, detecting and diagnosing faults, learning, reasoning and supporting decisions. As the size of CB increases, the accuracy of the system too increases leading to an increase in computational time complexity. So CBR techniques are coupled with machine learning approaches to reduce the same. This paper proposes a CBR methodology based Intelligent Car Fault Diagnosis System (CFDS) that integrates decision tree as a machine learning technique and jaccard similarity method to diagnose faults of cars accurately in a minimum time. A car fault diagnosis and detection system requires individual expertise gathered from personal experience and technical skills. Many times, not only the fault, but also the car part from where the fault originates or the cause of the fault needs to be known to handle or repair the problem. So, to help car mechanics as an assistant tool, CFDS is proposed; so that they can deal with various types of car faults very easily. Here, the proposed methodology integrates decision trees and jaccard similarity method to diagnose faults where the usage of decision trees is to store cases and jaccard similarity method is used to calculate the similarity percentages between user new query case and stored cases in the CB. User can post a new query about his car problem to the user interface of the CFDS. The CFDS uses the proposed methodology to find the solutions of that problem, and finally then at last these solutions are displayed to the user. To obtain better performance of the CFD system, this paper introduces a novel model of CBR cycle called CR4 model that is slightly modified version of traditional CBR cycle of R4 model, proposed by Aamodt and Plaza in 1994.
Index Terms: CBR, CR4 Model, CFDS, Case Base (CB), Car Description Decision tree (CDDT), Car Fault Description Decision tree (CFDDT), Case Clustering, Jaccard Similarity Method, User Query, Feedback.

Scope of the Article: Evolutionary Computing and Intelligent Systems