Failure mode identification for dynamic stochastic systems using operator norm and parameter space sample search
Abstract
To ensure the safety and reliability of systems subjected to random excitations such as earthquakes and strong winds, it is crucial to efficiently identify the dominant failure modes of these systems. However, existing methods often exhibit low efficiency in exploring the parameter space and rely on cumbersome procedures for classifying sample failure modes, leading to prohibitively high computational costs that severely limit their applicability in engineering practice. To address these issues, this paper proposes a novel method that efficiently determines the failure-mode category of each parameter space sample point under random excitations by extending operator norm theory towards non-Gaussian excitations, and using it to perform systematic classification. Furthermore, an improved constrained boundary sampling strategy is developed, enabling the sampling process not only to refine the boundaries between different failure modes but also to detect potential failure modes that may arise in sparsely sampled regions. Finally, a support vector machine is employed to separate the sample points belonging to different failure categories, thereby obtaining the boundaries between failure modes. The effectiveness and accuracy of the proposed approach are demonstrated through four representative numerical examples, which show that the method can reliably and efficiently identify the dominant failure modes under random excitations.
Details
- Organisationseinheit(en)
-
Institut für Risiko und Zuverlässigkeit
- Externe Organisation(en)
-
Changsha University of Science and Technology
The University of Liverpool
Tongji University
Technische Universität Dortmund
- Typ
- Artikel
- Journal
- Reliability Engineering and System Safety
- Band
- 272
- ISSN
- 0951-8320
- Publikationsdatum
- 08.2026
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Sicherheit, Risiko, Zuverlässigkeit und Qualität, Wirtschaftsingenieurwesen und Fertigungstechnik
- Elektronische Version(en)
-
https://doi.org/10.1016/j.ress.2026.112643 (Zugang:
Geschlossen
)
