Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain. Pirasteh, P., Nowaczyk, S., Pashami, S., Löwenadler, M., Thunberg, K., Ydreskog, H., & Berck, P. In Proceedings of the Workshop on Interactive Data Mining, of WIDM'19, pages 1–10, New York, NY, USA, February, 2019. Association for Computing Machinery. ![Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{pirasteh_interactive_2019,
address = {New York, NY, USA},
series = {{WIDM}'19},
title = {Interactive feature extraction for diagnostic trouble codes in predictive maintenance: {A} case study from automotive domain},
isbn = {978-1-4503-6296-2},
shorttitle = {Interactive feature extraction for diagnostic trouble codes in predictive maintenance},
url = {https://dl.acm.org/doi/10.1145/3304079.3310288},
doi = {10.1145/3304079.3310288},
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.},
urldate = {2023-05-21},
booktitle = {Proceedings of the {Workshop} on {Interactive} {Data} {Mining}},
publisher = {Association for Computing Machinery},
author = {Pirasteh, Parivash and Nowaczyk, Slawomir and Pashami, Sepideh and Löwenadler, Magnus and Thunberg, Klas and Ydreskog, Henrik and Berck, Peter},
month = feb,
year = {2019},
keywords = {Predictive maintenance, diagnostic trouble codes, failure detection, feature extraction},
pages = {1--10},
}
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