PSI - Issue 75

ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia (2025) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia (2025) 000 – 000 Available online at www.sciencedirect.com Procedia Structural Integrity 75 (2025) 262–275

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© 2025 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 the responsibility of Dr Fabien Lefebvre with at least 2 reviewers per paper One of the main objectives of NOMAD is to validate at any time the safety and estimate the remaining useful life (RUL) of sensitive equipment exposed to dynamic loads. After a representative observation monitoring period performed by NOMAD including real time calculation of the indicators related to the severity of the mechanical environment, the system can identify and classify different operating conditions, assigning them relative weightings based on their fatigue impact. This classification is then used to extrapolate the total damage over a target mission duration , providing a clear and reliable estimate of the equipment’s remaining life. To evaluate the robustness of this approach, we implemented and compared several prediction methods, including Monte Carlo simulation, a Central Limit Theorem-based method.. These methods were tested on real-world data collected from an accelerometer mounted on a vehicle driving on the highway representative of the mechanical environment at the foot of the equipment. This experiment aimed to validate the prediction models under realistic conditions and lay the groundwork for future tests on more complex environments. By combining real-time embedded processing with validated predictive models, NOMAD offers a powerful tool for in-service monitoring and maintenance planning. It helps to track equipment health in the field and make informed decisions to extend service life and prevent unexpected failures. One of the main objectives of NOMAD is to validate at any time the safety and estimate the remaining useful life (RUL) of sensitive equipment exposed to dynamic loads. After a representative observation monitoring period performed by NOMAD including real time calculation of the indicators related to the severity of the mechanical environment, the system can identify and classify different operating conditions, assigning them relative weightings based on their fatigue impact. This classification is then used to extrapolate the total damage over a target mission duration , providing a clear and reliable estimate of the equipment’s remaining life. To evaluate the robustness of this approach, we implemented and compared several prediction methods, including Monte Carlo simulation, a Central Limit Theorem-based method.. These methods were tested on real-world data collected from an accelerometer mounted on a vehicle driving on the highway representative of the mechanical environment at the foot of the equipment. This experiment aimed to validate the prediction models under realistic conditions and lay the groundwork for future tests on more complex environments. By combining real-time embedded processing with validated predictive models, NOMAD offers a powerful tool for in-service monitoring and maintenance planning. It helps to track equipment health in the field and make informed decisions to extend service life and prevent unexpected failures. Keywords: NOMAD ; Embedded system ; Fatigue analysis ; Fatigue Damage Spectrum (FDS) ; Remaining Useful Life (RUL) Abstract This paper presents Nomad, an embedded and autonomous system designed for real-time vibration monitoring and fatigue analysis of sensitive equipment. Unlike the conventional solution that rely on the lenghty data collection followed bu offline processing, Nomad continuously acquires vibrations signals, computes key damage indicators such as the Fatigue Damage Spectrum (FDS) and Extreme Response Spectrum (SRE). Abstract This paper presents Nomad, an embedded and autonomous system designed for real-time vibration monitoring and fatigue analysis of sensitive equipment. Unlike the conventional solution that rely on the lenghty data collection followed bu offline processing, Nomad continuously acquires vibrations signals, computes key damage indicators such as the Fatigue Damage Spectrum (FDS) and Extreme Response Spectrum (SRE). Fatigue Design 2025 (FatDes 2025) Monitoring the Mechanical Environment Harshness and Assessing the Remaining Life of Sensitive Equipment Fatigue Design 2025 (FatDes 2025) Monitoring the Mechanical Environment Harshness and Assessing the Remaining Life of Sensitive Equipment Mohamed El Yazrhi a , Jean-Yves Disson a a METRAVIB MVBE , 200 ch des Ormeaux, Limonest 69578, France Mohamed El Yazrhi a , Jean-Yves Disson a a METRAVIB MVBE , 200 ch des Ormeaux, Limonest 69578, France

Keywords: NOMAD ; Embedded system ; Fatigue analysis ; Fatigue Damage Spectrum (FDS) ; Remaining Useful Life (RUL)

2452-3216 © 2025 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 the responsibility of Dr Fabien Lefebvre with at least 2 reviewers per paper 10.1016/j.prostr.2025.11.028 2452-3216 © 2025 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 2025 organizers 2452-3216 © 2025 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 2025 organizers