Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major challenge. One crucial approach to achieve this, is predictive maintenance (PdM). Since modern vehicles come with an enormous amount of operating data, ML is an ideal candidate for PdM. While PdM and ML for automotive systems have both been covered in numerous review papers, there is no current survey on ML-based PdM for automotive systems. The number of publications in this field is increasing — underlining the need for such a survey. Consequently, the authors survey and categorize papers and analyse them from an application and ML perspective. Following that, they identify open challenges and discuss possible research directions. The authors conclude that (a) publicly available data would lead to a boost in research activities, (b) the majority of papers rely on supervised methods requiring labelled data, (c) combining multiple data sources can improve accuracies, (d) the use of deep learning methods will further increase but requires efficient and interpretable methods and the availability of large amounts of (labelled) data.
- Record URL:
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09518320
-
Supplemental Notes:
- © 2021 Andreas Theissler et al.
-
Authors:
- Theissler, Andreas
-
0000-0003-0746-0424
- Pérez-Velázquez, Judith
-
0000-0003-0969-075X
- Kettelgerdes, Marcel
-
0000-0002-4129-8320
- Elger, Gordon
- Publication Date: 2021-11
Language
- English
Media Info
- Media Type: Digital/other
- Features: Appendices; Figures; References; Tables;
- Pagination: 107864
-
Serial:
- Reliability Engineering & System Safety
- Volume: 215
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0951-8320
- Serial URL: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety
Subject/Index Terms
- TRT Terms: Machine learning; Maintenance management; Motor vehicle industry; Predictive models; Structural health monitoring; Vehicle maintenance
- Subject Areas: Highways; Maintenance and Preservation; Vehicles and Equipment;
Filing Info
- Accession Number: 01843865
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2022 3:51PM