---
_id: '27111'
abstract:
- lang: eng
  text: In the industry 4.0 era, there is a growing need to transform unstructured
    data acquired by a multitude of sources into information and subsequently into
    knowledge to improve the quality of manufactured products, to boost production,
    for predictive maintenance, etc. Data-driven approaches, such as machine learning
    techniques, are typically employed to model the underlying relationship from data.
    However, an increase in model accuracy with state-of-the-art methods, such as
    deep convolutional neural networks, results in less interpretability and transparency.
    Due to the ease of implementation, interpretation and transparency to both domain
    experts and non-experts, a rule-based method is proposed in this paper, for prognostics
    and health management (PHM) and specifically for diagnostics. The proposed method
    utilizes the most relevant sensor signals acquired via feature extraction and
    selection techniques and expert knowledge. As a case study, the presented method
    is evaluated on data from a real-world quality control set-up provided by the
    European prognostics and health management society (PHME) at the conference’s
    2021 data challenge. With the proposed method, our team took the third place,
    capable of successfully diagnosing different fault modes, irrespective of varying
    conditions.
author:
- first_name: Osarenren Kennedy
  full_name: Aimiyekagbon, Osarenren Kennedy
  id: '9557'
  last_name: Aimiyekagbon
- first_name: Lars
  full_name: Muth, Lars
  id: '77313'
  last_name: Muth
  orcid: 0000-0002-2938-5616
- first_name: Meike Claudia
  full_name: Wohlleben, Meike Claudia
  id: '43991'
  last_name: Wohlleben
  orcid: 0009-0009-9767-7168
- first_name: Amelie
  full_name: Bender, Amelie
  id: '54290'
  last_name: Bender
- first_name: Walter
  full_name: Sextro, Walter
  id: '21220'
  last_name: Sextro
citation:
  ama: 'Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics
    of a Production Line. In: Do P, King S, Fink O, eds. <i>Proceedings of the European
    Conference of the PHM Society 2021</i>. Vol 6. ; 2021:527-536. doi:<a href="https://doi.org/10.36001/phme.2021.v6i1.3042">10.36001/phme.2021.v6i1.3042</a>'
  apa: Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., &#38; Sextro,
    W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &#38;
    O. Fink (Eds.), <i>Proceedings of the European Conference of the PHM Society 2021</i>
    (Vol. 6, Issue 1, pp. 527–536). <a href="https://doi.org/10.36001/phme.2021.v6i1.3042">https://doi.org/10.36001/phme.2021.v6i1.3042</a>
  bibtex: '@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based
    Diagnostics of a Production Line}, volume={6}, DOI={<a href="https://doi.org/10.36001/phme.2021.v6i1.3042">10.36001/phme.2021.v6i1.3042</a>},
    number={1}, booktitle={Proceedings of the European Conference of the PHM Society
    2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike
    Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve
    and Fink, Olga}, year={2021}, pages={527–536} }'
  chicago: Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie
    Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In <i>Proceedings
    of the European Conference of the PHM Society 2021</i>, edited by Phuc Do, Steve
    King, and Olga Fink, 6:527–36, 2021. <a href="https://doi.org/10.36001/phme.2021.v6i1.3042">https://doi.org/10.36001/phme.2021.v6i1.3042</a>.
  ieee: 'O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based
    Diagnostics of a Production Line,” in <i>Proceedings of the European Conference
    of the PHM Society 2021</i>, 2021, vol. 6, no. 1, pp. 527–536, doi: <a href="https://doi.org/10.36001/phme.2021.v6i1.3042">10.36001/phme.2021.v6i1.3042</a>.'
  mla: Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production
    Line.” <i>Proceedings of the European Conference of the PHM Society 2021</i>,
    edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:<a href="https://doi.org/10.36001/phme.2021.v6i1.3042">10.36001/phme.2021.v6i1.3042</a>.
  short: 'O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P.
    Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM
    Society 2021, 2021, pp. 527–536.'
conference:
  name: PHM Society European Conference
date_created: 2021-11-03T12:26:39Z
date_updated: 2023-09-22T09:13:01Z
department:
- _id: '151'
doi: 10.36001/phme.2021.v6i1.3042
editor:
- first_name: Phuc
  full_name: Do, Phuc
  last_name: Do
- first_name: Steve
  full_name: King, Steve
  last_name: King
- first_name: Olga
  full_name: Fink, Olga
  last_name: Fink
intvolume: '         6'
issue: '1'
keyword:
- PHME 2021
- Feature Selection Classification
- Feature Selection Clustering
- Interpretable Model
- Transparent Model
- Industry 4.0
- Real-World Diagnostics
- Quality Control
- Predictive Maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://papers.phmsociety.org/index.php/phme/article/download/3042/1812
oa: '1'
page: 527-536
publication: Proceedings of the European Conference of the PHM Society 2021
publication_status: published
quality_controlled: '1'
status: public
title: Rule-based Diagnostics of a Production Line
type: conference
user_id: '9557'
volume: 6
year: '2021'
...