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 challeng...

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Vydáno v:Reliability engineering & system safety Ročník 215; s. 107864
Hlavní autoři: Theissler, Andreas, Pérez-Velázquez, Judith, Kettelgerdes, Marcel, Elger, Gordon
Médium: Journal Article
Jazyk:angličtina
Vydáno: Barking Elsevier Ltd 01.11.2021
Elsevier BV
Témata:
Artificial intelligence
Automobile industry
Automotive
Automotive fuels
Condition monitoring
Deep learning
Industrial development
Learning algorithms
Life cycles
Lifetime prediction
Machine learning
Maintenance costs
Polls & surveys
Predictive maintenance
Product life cycle
Product safety
Reliability
Reliability engineering
Vehicle
Deep learning
Vehicle
Lifetime prediction
Condition monitoring
Machine learning
Reliability
Artificial intelligence
Automotive
Predictive maintenance
ISSN:0951-8320, 1879-0836
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Shrnutí: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, we survey and categorize papers and analyse them from an application and ML perspective. Following that, we identify open challenges and discuss possible research directions. We 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. •Machine learning subfields overview relevant for automotive predictive maintenance•The aim is to make the field accessible to maintenance or machine learning experts•Machine learning-enabled predictive maintenance for automotive systems paper survey•Papers categorization of machine learning-enabled predictive maintenance in automotive industry•Use case- and a machine learning-perspective•Identification of open challenges and discussion of possible research directions.•This may serve readers to identify open research questions.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.107864