Unsupervised Machine Learning and Data mining Techniques for Telematics data to uncover fault behaviour
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
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