Coen van Gulijk - Reliability Engineering and Computational Intelligence

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This volume addresses the fusion between two scientific domains: reliability engineering and computational intelligence. Reliability engineering is an established domain that has a very good practical and scientific background for the analysis of the reliability of systems. Computational intelligence is relatively new in reliability engineering. But it has been an equally well-established branch of research with many groups over the world attempting to develop useful computational intelligence tools in different fields. Today, the continuous drive for digitalization causes reliability engineering and computational intelligence to merge. Combining the fields paves the way to progress on big data analytics, uncertain information evaluation, reasoning, prediction, modeling, optimization, decision making, and of course: more reliable systems.

The RECI workshop was held to discuss the merger of reliability engineering and computational intelligence and progress in the field. The workshop was an online scientific event held from October 2729, 2020 ( htps://ki.fri.uniza.sk/RECI2020/ ). This volume combines several papers submitted to the workshop. Out of 68 submissions for paper, 47 were selected for discussion and after review by 2 or 3 peers, 18 were accepted for publication in this volume.

Although there was some overlap in the papers, progress tended to cluster around three subjects: mathematical methods for RECI, digital techniques for RECI, and progress in RECI applications of which most are based on image analysis. The works in this volume are organized according to these clusters. The first cluster focuses on mathematical techniques and is entitled Mathematical and computational intelligence methods in reliability engineering. This cluster contains papers explaining system reliability techniques based on logic differential calculus and Hesse diagrams, survival signatures, fuzzy random forests, syntax trees, failure-based prevention models, and filtering algorithms for convolutional neural networks.

The second cluster focuses on subjects for digital techniques that are equally varied discussing: Digital technologies in reliability engineering. Starting from relatively abstract insights of how digital modeling technologies could replace mathematical modeling, building safety assurance cases for software, and a business approach to RECI. And moving into practicalities for hyperconverged systems, reliability of distributed processing and energy optimization of IoT systems.

Progress on image analysis is reported on: image superresolution, weights standardization in neural network techniques, deep learning, autonomous units for reliable monitoring, and complex monitoring systems (though the last two dont just treat image analysis). And last but not least progress is reported on Covid-19. This last cluster is entitled: Image analysis and other applications of computational intelligence in reliability engineering.

We believe that the work recorded in this volume is relevant for scientists interested in fusing problems of reliability engineering and computational intelligence. Whether they be researchers from an academic or business background, whether they be dealing with risk and reliability analysis or computational intelligence, we believe this work contributes to their understanding of the subject. We believe that the work is equally useful for IT companies traditionally working on computational intelligence, and developers of engineering systems traditionally working on reliability engineering.

We thank the local organizers for their efforts in making the workshop a successful one. We thank all authors and reviewers for their excellent contributions and we thank our funders through the Advanced Centre for Ph.D. Students and Young Researchers in InformaticsACeSYRI (610166-EPP-1-2019-1-SK-EPPKA2-CBHE-JP) of the European Unions Erasmus+ programme and New Methods Development for Reliability Analysis of Complex System (APVV-18-0027) of the Slovak Research and Development Agency.