Mining Sensor Data for Predictive Maintenance in the Automotive Industry

Giobergia, Flavio; Baralis, Elena; Camuglia, Maria; Cerquitelli, Tania; Mellia, Marco; Neri, Alessandra; Tricarico, Davide; Tuninetti, Daniela

doi:10.1109/dsaa.2018.00046

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) 2018

DOI: 10.1109/dsaa.2018.00046

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Mining Sensor Data for Predictive Maintenance in the Automotive Industry

Flavio Giobergia

Elena Baralis

Maria Camuglia³

et al.

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Cited by 14 publications

References 11 publications

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The asset administration shell as enabler for predictive maintenance: a review

Rahal,

Schwarz,

Sahelices

et al. 2023

J Intell Manuf

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The emergence of the Internet of Things and the interconnection of systems and machines enables the idea of Industry 4.0, a new industrial paradigm with a strong focus on interaction and communication between physical and digital entities, leading to the creation of cyber-physical systems. The digital twin and the standard for the Asset Administration Shell are concepts derived from Industry 4.0 that exploit the advantages of connecting the physical and virtual domains, improving the management and display of the collected data. Furthermore, the increasing availability of data has enabled the implementation of data-driven approaches, such as machine and deep learning models, for predictive maintenance in industrial and automotive applications. This paper provides a two-dimensional review of the Asset Administration Shell and data-driven methods for predictive maintenance, including fault diagnosis and prognostics. Additionally, a digital twin architecture combining the Asset Administration Shell, predictive maintenance and data-driven methods is proposed within the context of the WaVe project.

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The asset administration shell as enabler for predictive maintenance: a review

Rahal,

Schwarz,

Sahelices

et al. 2023

J Intell Manuf

View full text Add to dashboard Cite

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Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case

Giordano

Giobergia

Pastor

et al. 2022

Computers in Industry

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Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review

Turner

Okorie

Emmanouilidis

et al. 2022

Computers in Industry

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Mining Sensor Data for Predictive Maintenance in the Automotive Industry

Cited by 14 publications

References 11 publications

The asset administration shell as enabler for predictive maintenance: a review

The asset administration shell as enabler for predictive maintenance: a review

Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case

Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review

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