Browsing by Author "Zio, Enrico"

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    An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies
    (SAGE Publications, 2019) Mellal, Mohamed Arezki; Zio, Enrico
    This article presents an algorithm for optimal redundancy and repair team allocation with respect to minimum system cost and a system availability constraint. Four scenarios are considered for the failures occurring in the subsystems of the system: independence, linear dependence, weak dependence, and strong dependence. An adaptive cuckoo optimization algorithm is developed for solving the nonlinear integer optimization problem of allocation. A series–parallel system with six subsystems is considered as a case study for demonstration purposes. The results obtained highlight the good performance of the developed algorithm
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    A novel approach for remaining useful life prediction of high-reliability equipment based on long short-term memory and multi-head self-attention mechanism
    (Wiley-Blackwell, 2024) Al-Dahidi, Sameer; Rashed, Mohammad; Abu-Shams, Mohammad; Mellal, Mohamed Arezki; Alrbai, Mohammad; Ramadan, Saleem; Zio, Enrico
    Accurate prediction of the Remaining Useful Life (RUL) of components and systems is crucial for avoiding an unscheduled shutdown of production by planning maintenance interventions effectively in advance. For high-reliability equipment, few complete-run-to-failure trajectories may be available in practice. This constitutes a technical challenge for data-driven techniques for estimating the RUL. This paper proposes a novel data-driven approach for fault prognostics using the Long-Short Term Memory (LSTM) model combined with the Multi-Head Self-Attention (MHSA) mechanism. The former is applied to the input signals, whereas the latter is used to extract features from the LSTM hidden states, benefiting from the information from all hidden states rather than utilizing that of the final hidden state only. The proposed approach is characterized by its capability to recognize long-term dependencies while extracting features in both global and local contexts. This enables the approach to provide accurate RUL estimates in various stages of the equipment's life. The proposed approach is applied to an artificial case study simulated to mimic the realistic degradation behaviour of a heterogeneous fleet of aluminium electrolytic capacitors used in the automotive industry (under variable operating and environmental conditions). Results indicate that the proposed approach can provide accurate RUL estimates for high-reliability equipment compared to four benchmark models from the literature.
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    A penalty guided stochastic fractal search approach for system reliability optimization
    (Elsevier, 2016) Mellal, Mohamed Arezki; Zio, Enrico
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    System design optimization with mixed subsystems failure dependencies
    (Elsevier, 2023) Mellal, Mohamed Arezki; Zio, Enrico; Al-Dahidi, Sameer; Masuyama, Naoki; Nojima, Yusuke
    Systems present dependencies among their components failure behavior, which impact their ultimate availability. Previous works addressed the optimal design of systems in relation to its cost and under given availability constraint, considering identical subsystems failure dependencies. The present paper addresses this problem in a realistic scenario by taking into consideration mixed subsystems failure dependencies. The problem is formulated with reference to a complex bridge network system and a series-parallel system. Three nature-inspired optimization techniques are implemented to solve the problem, namely differential evolution (DE), manta ray foraging optimization (MRFO), and shuffled frog leaping algorithm (SFLA) with constraint handling. A numerical evaluation is performed; the results show that DE outperforms MRFO and SFLA