PSI - Issue 38

Available online at www.sciencedirect.com Available onlin at www.sci n edirect.com ScienceDirect Structural Integrity Procedia 00 (2021) 000 – 000 ScienceDirect Structural Integrity Procedia 00 (2021) 000 – 000

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www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 38 (2022) 12–29

© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2021 Organizers © 2021 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2021 Organizers We propose a general framework for exploring data which is inspired from the field of data science. Using a combination of engineering calculation packages and open-source data science tools, we show how engineers can further their understanding of the problem domain. We will present two case studies, each involving a different automotive component that is extensively instrumented with strain gauges, accelerometers and other sensors. We evaluate absolute damage from the strain data and, by using engineering indicators, data reduction, visualisations, correlations, we show how a minimal instrumentation subset can be identified for the purpose of damage approximation. © 2021 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) P er-review under responsibility of the scientific comm ttee of the Fatigue Design 2021 Organizers FATIGUE DESIGN 2021, 9th Edition of the International Conference on Fatigue Design Fatigue Damage estimation in vehicle from minimal instrumentation thanks to a mixed Engineering / Data Science approach Frédéric Kihm a , Tudor Miu a ,Marco Bonato b * a Hottinger Bruel & Kjaer France SAS, 46 rue du Champoreux, 91540 Mennecy, France b VALEO Thermal Systems, 8 rue Louis Lormand, 78322 La Verrière, France Abstract Monitoring damage and its potential causes in lab-tested structures requires extensive instrumentation that cannot be feasibly replicated in production assets. Since strain gauge instrumentation at a large scale is impractical, other proxy measurements for damage such as pressure, temperature or acceleration can be monitored and damage can be inferred from them. There is considerable difficulty in understanding not only which events lead to significant damage accumulation, but also in suitably and economically instrumenting assets in order to capture these data. We propose a general framework for exploring data which is inspired from the field of data science. Using a combination of engineering calculation packages and open-source data science tools, we show how engineers can further their understanding of the problem domain. We will present two case studies, each involving a different automotive component that is extensively instrumented with strain gauges, accelerometers and other sensors. We evaluate absolute damage from the strain data and, by using engineering indicators, data reduction, visualisations, correlations, we show how a minimal instrumentation subset can be identified for the purpose of damage approximation. FATIGUE DESIGN 2021, 9th Edition of t e International Co ference on Fatigue Design Fatigue Damage estimation in vehicle from minimal instrumentation thanks to a mixed Engineering / Data Science approach Frédéric Kihm a , Tudor Miu a ,Marco Bonato b * a Hottinger Bruel & Kjaer France SAS, 46 rue du Champoreux, 91540 Mennecy, France b VALEO Thermal Systems, 8 rue Louis Lormand, 78322 La Verrière, France Abstract Monitoring damage and its potential causes in lab-tested structures requires extensive instrumentation that cannot be feasibly replicated in production assets. Since strain gauge instrumentation at a large scale is impractical, other proxy measurements for damage such as pressure, temperature or acceleration can be monitored and damage can be inferred from them. There is considerable difficulty in understanding not only which events lead to significant damage accumulation, but also in suitably and economically instrumenting assets in order to capture these data. Keywords: Fatigue, Strain Gage, Data Science, Machine Learning

* Corresponding author. Tel.: +33 624 445 763 E-mail address: Fred.kihm@hbkworld.com Keyw d : Fatigue, S rain Gage, Data Science, Machine Learning

* Corresponding author. Tel.: +33 624 445 763 E-mail address: Fred.kihm@hbkworld.com

* Corresponding author. Tel.: +33 130182020; fax: =33 130182019. E-mail address: Fred.kihm@hbmprenscia.com

2452-3216 © 2021 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2021 Organizers 2452-3216 © 2021 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2021 Organizers * Corresponding author. Tel.: +33 130182020; fax: =33 130182019. E-mail address: Fred.kihm@hbmprenscia.com

2452-3216 © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2021 Organizers 10.1016/j.prostr.2022.03.003