Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Modeling and Optimization of Electrical Systems

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 25829

Share This Special Issue

Special Issue Editor

Prof. Dr. José Manuel Torres Farinha

E-Mail Website
Guest Editor

RCM2+ Research Centre for Asset Management and Systems Engineering, ISEC/IPC, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Interests: asset management; industrial maintenance; predictive maintenance
Special Issues, Collections and Topics in MDPI journals

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 2600 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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

17 pages, 6161 KiB

Open AccessArticle

Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning

by João Antunes Rodrigues, Alexandre Martins, Mateus Mendes, José Torres Farinha, Ricardo J. G. Mateus and Antonio J. Marques Cardoso

Energies 2022, 15(24), 9387; https://doi.org/10.3390/en15249387 - 12 Dec 2022

Cited by 5 | Viewed by 1636

Abstract

Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research is that the found classification works only carried out classifications of present, short-term, or mid-term failures. To close this gap, the work presented in this paper conducts a long-term classification. Full article

(This article belongs to the Special Issue Modeling and Optimization of Electrical Systems)

► Show Figures

Figure 1

28 pages, 960 KiB

Open AccessArticle

Optimization Process Applied in the Thermal and Luminous Design of High Power LED Luminaires

by Jose Luiz F. Barbosa, Antonio P. Coimbra, Dan Simon and Wesley P. Calixto

Energies 2022, 15(20), 7679; https://doi.org/10.3390/en15207679 - 18 Oct 2022

Cited by 1 | Viewed by 1516

Abstract

This work proposes the design of an optimization method for high-power LED luminaires with the introduction of new evaluation metrics. A luminaire geometry computational method is deployed to conduct thermal and optical analysis. This current effort novels by designing a tool that enables the analysis of uniformity for individual luminaire over the target plane in accordance with international regulatory standards. Additionally, adequate thermal management is conducted to guarantee nominal operation standard values determined by LED vendors. The results of this optimization method present luminaire models with different geometries that allow the stabilization of the temperature within the safety and uniform illuminance distribution thresholds. The resulting solution proposes the design of a

2 \times 2

HP-LED rectangular luminaire. During simulations, the temperature of the LED reaches a maximum value of

73.9

^{\circ}

C in a steady state with a uniform index of

0.228

for its individual luminaire. The overall uniform index identified for two separate and adjacent luminaire points in a pedestrian walk is

0.5413

with a minimal illuminance of

36.95

lx, maximum illuminance of

93.65

lx and average illuminance of

68.27

lx. Overall, we conclude that the currently adopted metric, which takes into consideration only the ratio between the minimum and the average illuminance, is not efficient and it cannot distinguish different luminaire geometry standards according to their uniform illuminance distribution. The metric proposed and designed in this work is capable of evaluating illuminance and thermal threshold criteria, as well as classifying different sorts of luminaries. Full article

(This article belongs to the Special Issue Modeling and Optimization of Electrical Systems)

► Show Figures

Figure 1

16 pages, 7993 KiB

Open AccessArticle

Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition

by João Antunes Rodrigues, José Torres Farinha, Mateus Mendes, Ricardo J. G. Mateus and António J. Marques Cardoso

Energies 2022, 15(17), 6308; https://doi.org/10.3390/en15176308 - 29 Aug 2022

Cited by 9 | Viewed by 1859

Abstract

Forecasting has extreme importance in industry due to the numerous competitive advantages that it provides, allowing to foresee what might happen and adjust management decisions accordingly. Industries increasingly use sensors, which allow for large-scale data collection. Big datasets enable training, testing and application of complex predictive algorithms based on machine learning models. The present paper focuses on predicting values from sensors installed on a pulp paper press, using data collected over three years. The variables analyzed are electric current, pressure, temperature, torque, oil level and velocity. The results of XGBoost and artificial neural networks, with different feature vectors, are compared. They show that it is possible to predict sensor data in the long term and thus predict the asset’s behaviour several days in advance. Full article

(This article belongs to the Special Issue Modeling and Optimization of Electrical Systems)

► Show Figures

Figure 1

21 pages, 13653 KiB

Open AccessArticle

Stochastic versus Fuzzy Models—A Discussion Centered on the Reliability of an Electrical Power Supply System in a Large European Hospital

by Constâncio António Pinto, José Torres Farinha, Hugo Raposo and Diego Galar

Energies 2022, 15(3), 1024; https://doi.org/10.3390/en15031024 - 29 Jan 2022

Cited by 5 | Viewed by 2713

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 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)

► Show Figures

Figure 1

21 pages, 4414 KiB

Open AccessArticle

An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

by Hamzeh Soltanali, Mehdi Khojastehpour, José Torres Farinha and José Edmundo de Almeida e Pais

Energies 2021, 14(22), 7758; https://doi.org/10.3390/en14227758 - 19 Nov 2021

Cited by 25 | Viewed by 3826

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 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)

► Show Figures

Figure 1

21 pages, 2017 KiB

Open AccessArticle

Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

by Balduíno César Mateus, Mateus Mendes, José Torres Farinha, Rui Assis and António Marques Cardoso

Energies 2021, 14(21), 6958; https://doi.org/10.3390/en14216958 - 22 Oct 2021

Cited by 72 | Viewed by 7869

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 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)

► Show Figures

Figure 1

24 pages, 4598 KiB

Open AccessArticle

Optimizing the Life Cycle of Physical Assets through an Integrated Life Cycle Assessment Method

by José Edmundo de Almeida Pais, Hugo D. N. Raposo, José Torres Farinha, Antonio J. Marques Cardoso and Pedro Alexandre Marques

Energies 2021, 14(19), 6128; https://doi.org/10.3390/en14196128 - 26 Sep 2021

Cited by 14 | Viewed by 4378

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 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)

► Show Figures

Journal Menu

Journal Browser

Modeling and Optimization of Electrical Systems

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI