Publications:Interactive feature extraction for diagnostic trouble codes in predictive maintenance : A case study from automotive domain
From ISLAB/CAISR
| 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 |
| Journal | |
| 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 |
| DOI | http://dx.doi.org/10.1145/3304079.3310288 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1335754 |
| 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. |
