Elger Group paper: First in the list of most downloaded articles

A survey on predictive maintenance enabled by machine learning in the automotive industry

Predictive Maintenance subfields of selected papers (own categorization). Picture taken from the open access corresponding publication [1], it can be found at https://ars.els-cdn.com/content/image/1-s2.0-S0951832021003835-gr3_lrg.jpg.

Reliability Engineering & System Safety, a renowned Elsevier journal in the area of reliability of complex technological systems published recently a paper from the Elger-group on the topic “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry” and it has become the most downloaded paper in the last 90 days.  

A comprehensive survey was undertaken to describe a variety of applications in the automotive industry where predictive maintenance enabled by machine learning has been successfully applied, the selected works are very diverse ranging from tyres inspection to faults in electrical vehicle batteries. The machine learning methods extend from random forest, incremental learning to deep learning. Furthermore, open challenges and possible research directions are identified.

[1] Andreas Theissler, Judith Pérez-Velázquez, Marcel Kettelgerdes, Gordon Elger, Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry, Reliability Engineering & System Safety, Volume 215, 2021, 107864, ISSN 0951-8320.

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