E
Enrico Zio
Researcher at Polytechnic University of Milan
Publications - 1236
Citations - 31075
Enrico Zio is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Monte Carlo method & Computer science. The author has an hindex of 73, co-authored 1127 publications receiving 23809 citations. Previous affiliations of Enrico Zio include Mines ParisTech & École Normale Supérieure.
Papers
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Artificial intelligence for fault diagnosis of rotating machinery: A review
TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.
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Reliability engineering: Old problems and new challenges
TL;DR: The first recorded usage of the word reliability dates back to the 1800s, albeit referred to a person and not a technical system as discussed by the authors, and since then, the concept of reliability has become a pervasive attribute worth of both qualitative and quantitative connotations.
Book
The Monte Carlo Simulation Method for System Reliability and Risk Analysis
TL;DR: The Monte Carlo simulation method is comprehensively illustrated and a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling is given.
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Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation
TL;DR: This paper considers a continuously monitored multi-component system and uses a Genetic Algorithm for determining the optimal degradation level beyond which preventive maintenance has to be performed and considers a predictive model describing the evolution of the degrading system based on the use of Monte Carlo simulation.
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A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
TL;DR: Experimental results indicate that the proposed combined model can capture non-linear characteristics of WSTS, achieving better forecasting performance than single forecasting models, in terms of accuracy.