Simulation of Power Assets Management Process

Protalinsky, Oleg M.; Khanova, Anna; Shcherbatov, Ivan

doi:10.1007/978-3-030-12072-6_40

Cited by 16 publications

(2 citation statements)

References 7 publications

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“…+ [36,37] End-to-end engineering It describes a process that takes a system or service from beginning to end and delivers a complete functional solution, usually without needing to obtain anything from a third party. + [38,39] Smart factory…”

Section: Name Description Effect On Optimization Examplesmentioning

confidence: 99%

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

et al. 2021

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The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field.

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Section: Name Description Effect On Optimization Examplesmentioning

confidence: 99%

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

et al. 2021

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“…A similar approach is also applied for controlling the exhaust gas temperature of gas turbine engine and detecting wearing problems: integrating data recorded through different sensors of the turbine, allows the monitoring of the turbine's performance and fault detection [44]. Another application belonging to the energy field regards the analysis of temperature and irradiance values to predict the performance and failures of photovoltaic plants [45]; interestingly, as implemented in a power grid company, ANNs may also be integrated in ontological models to optimize the faults diagnostics and repairing processes [46].…”

Section: State Of the Art: Anns Applications In Ammentioning

confidence: 99%

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

2019

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In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.

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