Maintenance decisions in condition-based maintenance often involve choosing between replacement and repair. The high costs associated with replacement and the introduced uncertainty from the structure's assembly and disassembly have led to increased exploration of repair methodologies. However, repairs are often imperfect, leading to additional uncertainties in predicting the component's future health condition. In this work, a model that utilizes Bayesian inference to estimate the distribution of the structure's recovery after an imperfect repair based on Remaining Useful Life estimations is proposed. The novelty of the methodology comes from the development of an imperfect repair model by looking at it from an integration point of view under the Prognostics and Health Management umbrella that could work independently of the prognostic and decision-making phases, offering flexibility. Evaluation of the proposed model is conducted through tension-tension fatigue experiments on aerospace-grade aluminium open-hole coupons. Repair is performed via a rectangular carbon fiber reinforced polymer patch placed on each coupon.
This is a dataset related to the experimental setup of our work concerning imperfect repairs (paper under review). Additional details can be found in the README file provided.
" authors: - family-names: Komninos given-names: Panagiotis orcid: "https://orcid.org/0000-0002-4770-5185" - family-names: Galanopoulos given-names: George - family-names: Kontogiannis given-names: Thanos orcid: "https://orcid.org/0009-0001-6720-1267" - family-names: Eleftheroglou given-names: Nick orcid: "https://orcid.org/0000-0001-7495-8121" - family-names: Zarouchas given-names: D. (Dimitrios) orcid: "https://orcid.org/0000-0001-5177-5138" title: " Data underlying the publication: A Bayesian inference-based framework for modelling imperfect post-repair behavior of remaining useful life under uncertainty" keywords: version: 1 identifiers: - type: doi value: 10.4121/b7d8031f-95a6-4ccc-8c08-802e694a0f40.v1 license: CC BY 4.0 date-released: 2024-10-02