Papers Papers/2022 Papers Papers/2022



Authors: Luísa Macedo 1 ; Luís Miguel Matos 1 ; Paulo Cortez 1 ; André Domingues 1 ; Guilherme Moreira 2 and André Pilastri 3

Affiliations: 1 ALGORITMI Center, Dep. Information Systems, University of Minho, Guimarães, Portugal ; 2 Bosch Car Multimedia, Braga, Portugal ; 3 EPMQ - IT Engineering Maturity and Quality Lab, CCG ZGDV Institute, Guimarães, Portugal

Keyword(s): Explainable Artificial Intelligence, Maintenance Data, Regression, Remaining Useful Life (RUL).

Abstract: Under the Industry 4.0 concept, there is increased usage of data-driven analytics to enhance the production process. In particular, equipment maintenance is a key industrial area that can benefit from using Machine Learning (ML) models. In this paper, we propose a novel Remaining Useful Life (RUL) ML-based spare part prediction that considers maintenance historical records, which are commonly available in several industries and thus more easy to collect when compared with specific equipment measurement data. As a case study, we consider 18,355 RUL records from an automotive multimedia assembly company, where each RUL value is defined as the full amount of units produced within two consecutive corrective maintenance actions. Under regression modeling, two categorical input transforms and eight ML algorithms were explored by considering a realistic rolling window evaluation. The best prediction model, which adopts an Inverse Document Frequency (IDF) data transformation and the Random F orest (RF) algorithm, produced high-quality RUL prediction results under a reasonable computational effort. Moreover, we have executed an eXplainable Artificial Intelligence (XAI) approach, based on the SHapley Additive exPlanations (SHAP) method, over the selected RF model, showing its potential value to extract useful explanatory knowledge for the maintenance domain. (More)


Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Macedo, L.; Miguel Matos, L.; Cortez, P.; Domingues, A.; Moreira, G. and Pilastri, A. (2022). A Machine Learning Approach for Spare Parts Lifetime Estimation. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 765-772. DOI: 10.5220/0010903800003116

author={Luísa Macedo. and Luís {Miguel Matos}. and Paulo Cortez. and André Domingues. and Guilherme Moreira. and André Pilastri.},
title={A Machine Learning Approach for Spare Parts Lifetime Estimation},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},


JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - A Machine Learning Approach for Spare Parts Lifetime Estimation
SN - 978-989-758-547-0
IS - 2184-433X
AU - Macedo, L.
AU - Miguel Matos, L.
AU - Cortez, P.
AU - Domingues, A.
AU - Moreira, G.
AU - Pilastri, A.
PY - 2022
SP - 765
EP - 772
DO - 10.5220/0010903800003116
PB - SciTePress