https://orcid.org/0000-0003-3895-0717
Engineering systems are becoming increasingly complex. They need to meet advanced requirements, especially in mission-critical fields with a low failure tolerance. As unexpected failures during the designed lifespan of a system may lead to catastrophic consequences, their reliability modeling and assessment are of utmost importance. The reliability modeling should achieve the safety requirement at a reasonable confidence level to help decision-makers arrive at sound decisions in practice.
However, the modeling and assessment of real engineering systems often must be made in an environment with mixed uncertainty, due to either the inherent randomness (aleatory uncertainty) or a lack of knowledge (epistemic uncertainty). Particularly, available information for reliability modeling is often imperfect, arising from insufficient accumulated knowledge, biased prior information, limited field test data, etc. This makes reliability assessment considering imperfect information and mixed uncertainty an imperative yet challenging task.
This Special Section issue is a collection of the latest innovative ideas and cutting-edge research advances for complex engineering system reliability modeling and assessment, as well as their applications in practical industrial settings. Pang and Dai proposed a failure modes and effects analysis optimization method. They use the Pythagorean fuzzy language as the evaluation language, while the best worst method is used to calculate the weight of the evaluator. The tomada de-decisao iterativa multicriterio method is used for compromise calculation to obtain the risk ranking of failure modes. Arya et al. presented an integrated system for ensuring uninterrupted power supply to tethered high-altitude platform systems by strategically managing the repair and replenishment of batteries in a k-out-of-n system. The results provide insights into efficient battery management strategies for high-altitude platform systems, ensuring reliable power supply while minimizing costs. Mutar and Hassan presented a technique for calculating structural importance measures of multi-state system with binary-state components. The technique uses the survival signature of MSS instead of the structure function. It is based on logical differential calculus, specifically direct partial logical derivatives. Silva and Pereira presented a novel architecture entitled the heuristic-based recommendation system. The main goal is to provide resources capable of streamlining the process of actively handling abnormal situations. The developed capabilities have been applied to a real case study in the operation of a metro train system, and the results obtained indicate the value of the proposed method. Brown and Ferson demonstrated the benefits of applying mixed uncertainty quantification and analysis techniques to pressure vessel inspection and integrity assessment through a worked example, which shows how the epistemic and aleatory uncertainty in inspection data can be represented using an imprecise probability approach. Mallamo et al. developed a systematic framework that integrates interpretability, predictive accuracy, and uncertainty quantification. Their contribution is the use of the Technique for Order Preference by Similarity to Ideal Solution to rank and evaluate prognostic models based on accuracy, interpretability, and uncertainty. Constant et al. proposed a new Active learning Kriging method for Sequential Models. This model introduces a novel enrichment strategy within the well-known Active Kriging Monte Carlo Simulation framework, leveraging the sequential nature of the performance function.
We hope that readers will enjoy reading, as well as benefit from these papers. We would like to appreciate all the efforts the authors and reviewers made in contributing to this special section. We would also like to express our sincere gratitude to Professor Michael Beer, Editor-in-Chief of the journal for the strong support throughout the preparation of this Special Section.
