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Special Issue "Modeling and Optimization of Electrical Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Electrical Power System".

Deadline for manuscript submissions: 15 November 2022 | Viewed by 4602

Special Issue Editor

Prof. Dr. José Torres Farinha
E-Mail Website
Guest Editor
1. Department of Mechanical Engineering, Polytechnic of Coimbra, ISEC, 3045-093 Coimbra, Portugal
2. Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), Coimbra, Portugal
Interests: predictive maintenance; artificial intelligence; deep learning; dynamic modelling
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Special Issue Information

Dear Colleagues,

Power Electrical Systems, Electrical Equipment, Electromechanical Systems and, in general, all Physical Assets need to have Maintenance Interventions aiming to reach their maximum Availability.

The Maintenance Interventions can be planned or unplanned, being the first one the most important to guarantee the Physical Asset’s Reliability.

In the ambit of Planned Maintenance, the Condition Monitoring is extremely important, what is emphasized when it is used Prediction.

To support Prediction, they can be used the traditional Time Series Algorithms or Artificial Intelligence Algorithms, namely based on the several different approaches of Neural Networks.

The preceding approaches need Sensors to read the Physical Asset’s Condition, being these read off-line or on-line; these can be connected through Wire or Wireless, according to the specificity of each Physical Asset.

The Data collected from Sensors are transmitted to a Datacentre and, usually, they consist in a huge Data, usually called Big Data.

The Physical Assets also must be analysed carefully in order to guarantee or improve their Reliability; it is because the Dynamic Modelling is so important. Tools like Fault Trees, Markov Chains, Hidden Markov Chains, Petri Nets, among others are important knowledge pieces that are necessary to use aiming to evaluate and improve Physical Asset’s Reliability.

To manage the Physical Assets, usually, they are used tools that permit to reach high levels of efficiency, being the Lean Thinking a very important way for that.

Based on the preceding, it can be concluded that it is necessary to have an excellent organization and management of the Physical Assets in conjunction with the best Maintenance policies to reach the maximum Physical Asset’s Availability, in order to have their maximum productivity.

This Special Issue would like to encourage original contributions regarding the aspects preceding, but not necessary limited to them.

Prof. Dr. José Torres Farinha
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • maintenance
  • maintenance management
  • reliability
  • availability
  • condition monitoring
  • predictive maintenance
  • artificial intelligence
  • neural networks
  • dynamic modelling
  • asset management
  • lean thinking

Published Papers (4 papers)

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Research

Article
Stochastic versus Fuzzy Models—A Discussion Centered on the Reliability of an Electrical Power Supply System in a Large European Hospital
Energies 2022, 15(3), 1024; https://doi.org/10.3390/en15031024 - 29 Jan 2022
Viewed by 763
Abstract
This paper discusses the Reliability, Availability, Maintainability, and Safety (RAMS) of an electrical power supply system in a large European hospital. The primary approach is based on fuzzy logic and Petri nets, using the CPNTools software to simulate and determine the most important [...] Read more.
This paper discusses the Reliability, Availability, Maintainability, and Safety (RAMS) of an electrical power supply system in a large European hospital. The primary approach is based on fuzzy logic and Petri nets, using the CPNTools software to simulate and determine the most important modules of the system according to the Automatic Transfer Switch. Fuzzy Inference System is used to analyze and assess the reliability value. The stochastic versus fuzzy approach is also used to evaluate the reliability contribution of each system module. This case study aims to identify and analyze possible system failures and propose new solutions to improve the system reliability of the power supply system. The dynamic modeling is based on block diagrams and Petri nets and is evaluated via Markov chains, including a stochastic approach linked to the previous analysis. This holistic approach adds value to this type of research question. A new electrical power supply system design is proposed to increase the system’s reliability based on the results achieved. Full article
(This article belongs to the Special Issue Modeling and Optimization of Electrical Systems)
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Article
An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing
Energies 2021, 14(22), 7758; https://doi.org/10.3390/en14227758 - 19 Nov 2021
Cited by 3 | Viewed by 951
Abstract
Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program. To deal with such uncertainties, this study proposes a [...] Read more.
Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program. To deal with such uncertainties, this study proposes a fuzzy fault tree analysis (FFTA) approach as a proactive knowledge-based technique to estimate the FP towards a convenient maintenance plan in the automotive manufacturing industry. Furthermore, in order to enhance the accuracy of the FFTA model in predicting FP, the effective decision attributes, such as the experts’ trait impacts; scales variation; and assorted membership, and the defuzzification functions were investigated. Moreover, due to the undynamic relationship between the failures of complex systems in the current FFTA model, a Bayesian network (BN) theory was employed. The results of the FFTA model revealed that the changes in various decision attributes were not statistically significant for FP variation, while the BN model, that considered conditional rules to reflect the dynamic relationship between the failures, had a greater impact on predicting the FP. Additionally, the integrated FFTA–BN model was used in the optimization model to find the optimal maintenance intervals according to the estimated FP and total expected cost. As a case study, the proposed model was implemented in a fluid filling system in an automotive assembly line. The FPs of the entire system and its three critical subsystems, such as the filling headset, hydraulic–pneumatic circuit, and the electronic circuit, were estimated as 0.206, 0.057, 0.065, and 0.129, respectively. Moreover, the optimal maintenance interval for the whole filling system considering the total expected costs was determined as 7th with USD 3286 during 5000 h of the operation time. Full article
(This article belongs to the Special Issue Modeling and Optimization of Electrical Systems)
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Article
Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press
Energies 2021, 14(21), 6958; https://doi.org/10.3390/en14216958 - 22 Oct 2021
Cited by 3 | Viewed by 876
Abstract
The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long [...] Read more.
The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options. Full article
(This article belongs to the Special Issue Modeling and Optimization of Electrical Systems)
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Article
Optimizing the Life Cycle of Physical Assets through an Integrated Life Cycle Assessment Method
Energies 2021, 14(19), 6128; https://doi.org/10.3390/en14196128 - 26 Sep 2021
Viewed by 1061
Abstract
The purpose of this study was to apply new methods of econometric models to the Life Cycle Assessment (LCA) of physical assets, by integrating investments such as maintenance, technology, sustainability, and technological upgrades, and to propose a means to evaluate the Life Cycle [...] Read more.
The purpose of this study was to apply new methods of econometric models to the Life Cycle Assessment (LCA) of physical assets, by integrating investments such as maintenance, technology, sustainability, and technological upgrades, and to propose a means to evaluate the Life Cycle Investment (LCI), with emphasis on sustainability. Sustainability is a recurrent theme of existing studies and will be a concern in coming decades. As a result, equipment with a smaller environmental footprint is being continually developed. This paper presents a method to evaluate asset depreciation with an emphasis on the maintenance investment, technology depreciation, sustainability depreciation, and technological upgrade investment. To demonstrate the value added of the proposed model, it was compared with existing models that do not take the previously mentioned aspects into consideration. The econometric model is consistent with asset life cycle plans as part of the Strategic Asset Management Plan of the Asset Management System. It is clearly demonstrated that the proposed approach is new and the results are conclusive, as demonstrated by the presented models and their results. This research aims to introduce new methods that integrate the factors of technology upgrades and sustainability for the evaluation of assets’ LCA and replacement time. Despite the increase in investment in technology upgrades and sustainability, the results of the Integrated Life Cycle Assessment First Method (ILCAM1), which represents an improved approach for the analyzed data, show that the asset life is extended, thus increasing sustainability and promoting the circular economy. By comparison, the Integrated Life Cycle Investment Assessment Method (ILCIAM) shows improved results due to the investment in technology upgrades and sustainability. Therefore, this study presents an integrated approach that may offer a valid tool for decision makers. Full article
(This article belongs to the Special Issue Modeling and Optimization of Electrical Systems)
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