Issue |
Mechanics & Industry
Volume 26, 2025
Recent advances in vibrations, noise, and their use for machine monitoring
|
|
---|---|---|
Article Number | 22 | |
Number of page(s) | 19 | |
DOI | https://doi.org/10.1051/meca/2025014 | |
Published online | 01 July 2025 |
Original Article
Verifying hyperparameter sensitivities of optimal filter design methods for fault signature enhancement
1
Centre for Asset Integrity Management, Department of Mechanical and Aeronautical Engineering, University of Pretoria,
Pretoria,
South Africa
2
School of Mechanical, Industrial & Aeronautical Engineering, University of the Witwatersrand,
Johannesburg,
South Africa
3
Department of Mechanical Engineering, KU Leuven,
Celestijnenlaan 300,
3001
Heverlee,
Belgium
4
Flanders Make @ KU Leuven,
Belgium
* e-mail: stephan.schmidt@up.ac.za
Received:
7
March
2024
Accepted:
15
May
2025
Optimal filters are designed in vibration-based condition monitoring to enhance weak fault signatures for improved diagnosis. While optimisation-based filter design approaches have matured, their validation has typically focused on final objective values and constraint satisfaction. However, ensuring robust and reliable results requires verifying the correctness of design sensitivities, i.e., the gradients of both objective and constraint functions with respect to design variables, as well as hyperparameter sensitivities. This paper emphasises the importance of confirming and quantifying a filter’s response to varying hyperparameters to ensure it meets design specifications. By rigorously verifying sensitivities, engineers can more confidently deploy optimal filter designs that enhance fault-related features, resulting in more effective fault detection and diagnosis in complex engineering systems.
Key words: Optimal digital filter design / gearbox fault detection / squared envelope spectrum / gradient-based optimisation
© S. Schmidt et al., Published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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