Reliability Engineering & System Safety· 2021Q1
Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
- 501citations
- Q1SCImago
- 2021year
Short summary
A survey of machine learning-based predictive maintenance (PdM) in the automotive industry reveals that publicly available data and supervised learning methods are key limitations, while multi-source data fusion and deep learning show promise.
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Key points
- Machine learning is ideal for predictive maintenance in modern vehicles due to vast operating data.
- A significant challenge is the scarcity of publicly available labeled data for training ML models.
- Combining multiple data sources can enhance the accuracy of predictive maintenance models.
- Deep learning methods show potential but require efficient, interpretable approaches and large datasets.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Reliability Engineering & System Safety, 2021 · DOI ↗
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Field: Safety, Risk, Reliability and Quality
Safety, Risk, Reliability and QualityEngineering