Automotive industry is focused on monitoring health and performance of vehicles to help customers to improve uptime and reduce the downtime with planned maintenance. To achieve this, technologies like telematics are used for continuous data flow. The data depicts the details of functioning of various components in the engine and subsystems; and the fault codes - the diagnostic troubleshooting codes, associated with them. Traditional method by service representatives to address these fault codes- is to follow the recommended troubleshooting trees. With the electronic engine and subsystems performance interdependencies, it gets challenging to address the same in the traditional manner. Also, the grouping of the fault codes is not known if the fault codes that occurred are related to a specific cause. We implemented unsupervised machine learning and data mining techniques to address such issues. First, the co-occurrence theory that helps in understanding the fault codes that occur together and exhibit dependencies and relations amongst them. We implemented market basket analysis to study the fault codes co-occurrences. Second, we implemented clustering algorithms to know groups/categories of fault codes based on their functional states. These studies provide insights on the failures, component states, and help in troubleshooting the problems experienced by the engine and subsystems. Also, these methods help to address the issues in the early stage, in turn helping technicians to identify the issue and improving the uptime (early repairs and diagnostics). Further, this paper presents the results of the experiments aligning to the domain needs.
How to Cite
Machine Learing, Data Mining, Telematics, Automotive Industry, Fault Code Bahaviour, Troubleshooting, Unsupervised, Fault Codes, Co-occurrence Theory, Market Basket Analysis, Diagnostics
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.