Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case. Giordano, D., Giobergia, F., Pastor, E., La Macchia, A., Cerquitelli, T., Baralis, E., Mellia, M., & Tricarico, D. Computers in Industry, 134:103554, January, 2022. ![Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Predictive maintenance is an ever-growing topic of interest, spanning different fields and approaches. In the automotive domain, thanks to on-board sensors and the possibility to transmit collected data to the cloud, car manufacturers can deploy predictive maintenance solutions to prevent components malfunctioning and eventually recall to the service the vehicle before the customer experiences the failure. In this paper we present PREPIPE, a data-driven pipeline for predictive maintenance. Given the raw time series of signals recorded by the on-board engine control unit of diesel engines, we exploit PREPIPE to predict the clogging status of the oxygen sensor, a key component of the exhaust system to control combustion efficiency and pollutant emissions. In the design of PREPIPE we deeply investigate: (i) how to choose the best subset of signals to best capture the sensor status, (ii) how much data needs to be collected to make the most accurate prediction, (iii) how to transform the original time series into features suitable for state-of-art classifiers, (iv) how to select the most important features, (v) how to include historical features to predict the clogging status of the sensor. We thoroughly assess PREPIPE performance and compare it with state-of-art deep learning architectures. Our results show that PREPIPE correctly identifies critical situations before the sensor reaches critical conditions. Furthermore, PREPIPE supports domain experts in optimizing the design of data-driven predictive maintenance pipelines with performance comparable to deep learning methodologies while keeping a degree of interpretability.
@article{giordano_data-driven_2022,
title = {Data-driven strategies for predictive maintenance: {Lesson} learned from an automotive use case},
volume = {134},
issn = {0166-3615},
shorttitle = {Data-driven strategies for predictive maintenance},
url = {https://www.sciencedirect.com/science/article/pii/S0166361521001615},
doi = {10.1016/j.compind.2021.103554},
abstract = {Predictive maintenance is an ever-growing topic of interest, spanning different fields and approaches. In the automotive domain, thanks to on-board sensors and the possibility to transmit collected data to the cloud, car manufacturers can deploy predictive maintenance solutions to prevent components malfunctioning and eventually recall to the service the vehicle before the customer experiences the failure. In this paper we present PREPIPE, a data-driven pipeline for predictive maintenance. Given the raw time series of signals recorded by the on-board engine control unit of diesel engines, we exploit PREPIPE to predict the clogging status of the oxygen sensor, a key component of the exhaust system to control combustion efficiency and pollutant emissions. In the design of PREPIPE we deeply investigate: (i) how to choose the best subset of signals to best capture the sensor status, (ii) how much data needs to be collected to make the most accurate prediction, (iii) how to transform the original time series into features suitable for state-of-art classifiers, (iv) how to select the most important features, (v) how to include historical features to predict the clogging status of the sensor. We thoroughly assess PREPIPE performance and compare it with state-of-art deep learning architectures. Our results show that PREPIPE correctly identifies critical situations before the sensor reaches critical conditions. Furthermore, PREPIPE supports domain experts in optimizing the design of data-driven predictive maintenance pipelines with performance comparable to deep learning methodologies while keeping a degree of interpretability.},
language = {en},
urldate = {2021-11-15},
journal = {Computers in Industry},
author = {Giordano, Danilo and Giobergia, Flavio and Pastor, Eliana and La Macchia, Antonio and Cerquitelli, Tania and Baralis, Elena and Mellia, Marco and Tricarico, Davide},
month = jan,
year = {2022},
keywords = {Automotive, Data-driven, Machine learning, Predictive maintenance, Time series},
pages = {103554},
}
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In the design of PREPIPE we deeply investigate: (i) how to choose the best subset of signals to best capture the sensor status, (ii) how much data needs to be collected to make the most accurate prediction, (iii) how to transform the original time series into features suitable for state-of-art classifiers, (iv) how to select the most important features, (v) how to include historical features to predict the clogging status of the sensor. We thoroughly assess PREPIPE performance and compare it with state-of-art deep learning architectures. Our results show that PREPIPE correctly identifies critical situations before the sensor reaches critical conditions. 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