Print Email Facebook Twitter Particle filter-based fatigue damage prognosis by fusing multiple degradation models Title Particle filter-based fatigue damage prognosis by fusing multiple degradation models Author Li, Tianzhi (Politecnico di Milano) Chen, Jian (Nanjing University of Aeronautics and Astronautics) Yuan, Shenfang (Nanjing University of Aeronautics and Astronautics) Zarouchas, D. (TU Delft Group Zarouchas) Sbarufatti, Claudio (Politecnico di Milano) Cadini, Francesco (Politecnico di Milano) Date 2024 Abstract Fatigue damage prognosis always requires a degradation model describing the damage evolution with time; thus, the prognostic performance highly depends on the selection of such a model. The best model should probably be case specific, calling for the fusion of multiple degradation models for a robust prognosis. In this context, this paper proposes a scheme of online fusing multiple models in a particle filter (PF)-based damage prognosis framework. First, each prognostic model has its process equation built through a physics-based or data-driven degradation model and has its measurement equation linking the damage state and the measurement. Second, each model is independently processed through one PF to provide one group of particles. Then, the particles from all models are adopted for remaining useful life prediction. Finally, the particles from each PF are fused with those from all the other PFs to improve their particle diversity, and consequently, to provide better estimation and prognostic performance. The feasibility and robustness of the proposed method are validated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a guided wave measurement system. Subject damage prognosisdegradation modelfusionLamb wavesparticle diversityparticle filterStructural health monitoring To reference this document use: http://resolver.tudelft.nl/uuid:2a089659-6695-49b2-894e-3647f95f79fa DOI https://doi.org/10.1177/14759217231216697 ISSN 1475-9217 Source Structural Health Monitoring: an international journal Part of collection Institutional Repository Document type journal article Rights © 2024 Tianzhi Li, Jian Chen, Shenfang Yuan, D. Zarouchas, Claudio Sbarufatti, Francesco Cadini Files PDF li-et-al-2024-particle-fi ... models.pdf 6.83 MB Close viewer /islandora/object/uuid:2a089659-6695-49b2-894e-3647f95f79fa/datastream/OBJ/view