Publications:Interactive feature extraction for diagnostic trouble codes in predictive maintenance : A case study from automotive domain


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Title Interactive feature extraction for diagnostic trouble codes in predictive maintenance : A case study from automotive domain
Author Parivash Pirasteh and Sławomir Nowaczyk and Sepideh Pashami and Magnus Löwenadler and Klas Thunberg and Henrik Ydreskog and Peter Berck
Year 2019
PublicationType Conference Paper
HostPublication Proceedings of the Workshop on Interactive Data Mining
Conference WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11-15 February, 2019
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Abstract Predicting future maintenance needs of equipment can be addressed in a variety of ways. Methods based on machine learning approaches provide an interesting platform for mining large data sets to find patterns that might correlate with a given fault. In this paper, we approach predictive maintenance as a classification problem and use Random Forest to separate data readouts within a particular time window into those corresponding to faulty and non-faulty component categories. We utilize diagnostic trouble codes (DTCs) as an example of event-based data, and propose four categories of features that can be derived from DTCs as a predictive maintenance framework. We test the approach using large-scale data from a fleet of heavy duty trucks, and show that DTCs can be used within our framework as indicators of imminent failures in different components.