Venue & metadata

• Journal/Proceedings: Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020
• Pages: 261 – 266
• Note: Cited by: 1
• Author keywords: Data-driven methods; Dependability Evaluation; Predictive maintenance; Quantitative Formal Models

Abstract

Modern predictive maintenance is the convergence of several technological trends: developing new techniques and algorithms can be very costly due to the need for a physical prototype. This research has the final aim to build a simulation-based software framework for modeling and analysing complex systems and for defining predictive maintenance algorithms. By the usage of simulation, quantitative evaluation of the dependability of such systems will be possible. The ERTMS/ETCS dependability case study is presented to prove the applicability of the software. © 2020 IEEE.

Keywords

Computer programming GS Maintenance GS Data driven GS Data-driven methods GS Dependability evaluation GS Flexible simulation GS Formal modeling GS Model-based OPC GS Predictive maintenance GS Quantitative formal model GS Software frameworks GS Technological trends GS Computer software GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Abate, C., Campanile, L., & Marrone, S. (2020). A flexible simulation-based framework for model-based/data-driven dependability evaluation [Conference paper]. Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020, 261–266. https://doi.org/10.1109/ISSREW51248.2020.00083