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

Authors:

Parivash Pirasteh,

Slawomir Nowaczyk,

Sepideh Pashami,

Magnus Löwenadler,

Henrik Ydreskog,

Peter BerckAuthors Info & Claims

WIDM'19: Proceedings of the Workshop on Interactive Data Mining

Article No.: 4, Pages 1 - 10

https://doi.org/10.1145/3304079.3310288

Published: 15 February 2019 Publication History

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.

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Cited By

Hafeez AAlonso ERiaz AHong JPark J(2024)DTC-TranGru: Improving the performance of the next-DTC Prediction Model with Transformer and GRUProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635962(927-934)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3635962
Amico BCombi CRizzi RSala P(2024)Predictive mining of multi-temporal relationsInformation and Computation10.1016/j.ic.2024.105228(105228)Online publication date: Oct-2024
https://doi.org/10.1016/j.ic.2024.105228
Samuel AAdeleke AAnosike-Francis EOgedengbe TIkubanni PAdeyemi FOdusote JOnuoha MShuaib U(2023)Advent of Artificial Intelligence in Automotive Engineering2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS)10.1109/ICMEAS58693.2023.10429907(1-6)Online publication date: 1-Nov-2023
https://doi.org/10.1109/ICMEAS58693.2023.10429907
Show More Cited By

Index Terms

Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning algorithms
      1. Feature selection
2. Hardware
  1. Robustness
    1. Fault tolerance
      1. Failure prediction

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Published In

cover image ACM Conferences

WIDM'19: Proceedings of the Workshop on Interactive Data Mining

February 2019

44 pages

ISBN:9781450362962

DOI:10.1145/3304079

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 15 February 2019

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February 15, 2019

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Cited By

Hafeez AAlonso ERiaz AHong JPark J(2024)DTC-TranGru: Improving the performance of the next-DTC Prediction Model with Transformer and GRUProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635962(927-934)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3635962
Amico BCombi CRizzi RSala P(2024)Predictive mining of multi-temporal relationsInformation and Computation10.1016/j.ic.2024.105228(105228)Online publication date: Oct-2024
https://doi.org/10.1016/j.ic.2024.105228
Samuel AAdeleke AAnosike-Francis EOgedengbe TIkubanni PAdeyemi FOdusote JOnuoha MShuaib U(2023)Advent of Artificial Intelligence in Automotive Engineering2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS)10.1109/ICMEAS58693.2023.10429907(1-6)Online publication date: 1-Nov-2023
https://doi.org/10.1109/ICMEAS58693.2023.10429907
Huang TChen PLi R(2022)A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online SystemsProceedings of the ACM Web Conference 202210.1145/3485447.3511984(1797-1806)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3511984
Hafeez AAlonso ERiaz A(2022)DTCEncoder: A Swiss Army Knife Architecture for DTC Exploration, Prediction, Search and Model Interpretation2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00085(519-524)Online publication date: Dec-2022
https://doi.org/10.1109/ICMLA55696.2022.00085
Turner COkorie OEmmanouilidis COyekan J(2022)Circular production and maintenance of automotive partsComputers in Industry10.1016/j.compind.2021.103593136:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.compind.2021.103593
Hafeez AAlonso ETer-Sarkisov A(2021)Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA52953.2021.00167(1016-1021)Online publication date: Dec-2021
https://doi.org/10.1109/ICMLA52953.2021.00167
Jwo JLin CLee C(2021)Smart technology–driven aspects for human-in-the-loop smart manufacturingThe International Journal of Advanced Manufacturing Technology10.1007/s00170-021-06977-9Online publication date: 2-Apr-2021
https://doi.org/10.1007/s00170-021-06977-9
Ferreira DScholz TPrytz R(2021)Importance Weighting of Diagnostic Trouble Codes for Anomaly DetectionMachine Learning, Optimization, and Data Science10.1007/978-3-030-64583-0_37(410-421)Online publication date: 8-Jan-2021
https://doi.org/10.1007/978-3-030-64583-0_37
Zhao NChen JWang ZPeng XWang GWu YZhou FFeng ZNie XZhang WSui KPei DDevanbu PCohen MZimmermann T(2020)Real-time incident prediction for online service systemsProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409672(315-326)Online publication date: 8-Nov-2020
https://dl.acm.org/doi/10.1145/3368089.3409672

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