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

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 17105

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Special Issue Editors

Dr. Tangbin Xia

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State Key Laboratory of Mechanical System & Vibration, Department of Industrial Engineering & Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: energy management; sustainable manufacturing; energy saving of manufacturing system; operation & maintenance of power generation; prognostics & health management of power system
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Prof. Dr. Ershun Pan

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Department of Industrial Engineering & Management, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: quality and reliability engineering; prognostics and health management; production planning; lean management
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Dr. Rongxi Wang

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State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, China
Interests: reliability and maintenance, quality control and performance condition evaluation of distributed and complex electromechanical system and the theory of complex network
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Dr. Yupeng Li

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Mechanical Engineering with School of Mines, China University of Mining and Technology, Xuzhou, China
Interests: product evolution control and operation management of manufacturing and service systems
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Dr. Xi Gu

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Department of Mechanical & Aerospace Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854, USA
Interests: smart manufacturing; reconfigurable manufacturing systems; production and maintenance decision-making; engineering education
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Special Issue Information

Dear Colleagues,

Meeting our current needs without sacrificing our ability to continue to do so in the future is a key sustainability concept which is increasingly becoming more challenging than ever, with the growing population rate,

As one of the experts in the field, you are cordially invited to submit your new papers to this Special Issue. We will publish your paper and will broadcast your brilliant ideas through communication media.

Detailed information regarding published articles, indexing, and editorial board members can be found at the journal website (https://www.mdpi.com/journal/energies).

The aims and scope include but are not limited to:

Energy management;
Green manufacturing;
Sustainable manufacturing;
Energy saving of manufacturing systems;
Operation and maintenance of power generation;
Prognostics and health management of power systems.

Dr. Tangbin Xia
Prof. Dr. Ershun Pan
Dr. Rongxi Wang
Dr. Yupeng Li
Dr. Xi Gu
Guest Editors

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

Energy management
Green manufacturing
Sustainable manufacturing
Energy saving of manufacturing systems
Operation and maintenance of power generation
Prognostics and health management of power systems

Published Papers (12 papers)

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Research

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18 pages, 2406 KiB

Open AccessArticle

Preventive Maintenance Strategy Optimization in Manufacturing System Considering Energy Efficiency and Quality Cost

by Liang Yang, Qinming Liu, Tangbin Xia, Chunming Ye and Jiaxiang Li

Energies 2022, 15(21), 8237; https://doi.org/10.3390/en15218237 - 04 Nov 2022

Cited by 5 | Viewed by 1639

Abstract

Climate change is a serious challenge facing the world today. Countries are already working together to control carbon emissions and mitigate global warming. Improving energy efficiency is currently one of the main carbon reduction measures proposed by the international community. Within this context, improving energy efficiency in manufacturing systems is crucial to achieving green and low-carbon transformation. The aim of this work is to develop a new preventive maintenance strategy model. The novelty of the model is that it takes into account energy efficiency, maintenance cost, product quality, and the impact of recycling defective products on energy efficiency. Based on the relationship between preventive maintenance cost, operating energy consumption, and failure rate, the correlation coefficient is introduced to obtain the variable preventive maintenance cost and variable operating energy consumption. Then, the cost and energy efficiency models are established, respectively, and finally, the Pareto optimal solution is found by the nondominated sorting genetic algorithm II (NSGAII). The results show that the preventive maintenance strategy proposed in this paper is better than the general maintenance strategy and more relevant to the actual situation of manufacturing systems. The scope of the research in this paper can support the decision of making energy savings and emission reductions in the manufacturing industry, which makes the production, maintenance, quality, and architecture of the manufacturing industry optimized. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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16 pages, 1002 KiB

Open AccessArticle

Evaluation of the Operational Efficiency and Energy Efficiency of Rail Transit in China’s Megacities Using a DEA Model

by Hao Zhang, Xinyue Wang, Letao Chen, Yujia Luo and Sujie Peng

Energies 2022, 15(20), 7758; https://doi.org/10.3390/en15207758 - 20 Oct 2022

Cited by 2 | Viewed by 1242

Abstract

To date, along with the rapid development of urban rail transit (URT) in China, the evaluation of operational efficiency and energy efficiency has become one of the most important topics. However, the extant literature regarding the efficiency of URT at the line level and considering carbon emissions is limited. To fill the gap, an evaluation model based on slacks-based measure (SBM) data envelopment analysis (DEA) is proposed to measure the efficiencies, which is applied to 61 URT lines in China’s four megacities. The findings are summarized as follows: (1) The average operational efficiency and energy efficiency of URT lines are low, and both have great room for improvement. (2) There are significant disparities in the efficiency of URT lines in the case cities. For instance, the average operational efficiency of URT lines in Guangzhou is higher than that of other cities, while the average energy efficiency of URT lines in Shanghai is higher than that of other cities. (3) The URT lines operated by state-owned enterprises have higher average operational efficiency, while the lines operated by joint ventures have higher average energy efficiency. Finally, some suggestions are provided to improve the efficiencies. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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17 pages, 2533 KiB

Open AccessArticle

A Mixed Algorithm for Integrated Scheduling Optimization in AS/RS and Hybrid Flowshop

by Jiansha Lu, Lili Xu, Jinghao Jin and Yiping Shao

Energies 2022, 15(20), 7558; https://doi.org/10.3390/en15207558 - 13 Oct 2022

Cited by 3 | Viewed by 1084

Abstract

The integrated scheduling problem in automated storage and retrieval systems (AS/RS) and the hybrid flowshop is critical for the realization of lean logistics and just-in-time distribution in manufacturing systems. The bi-objective model that minimizes the operation time in AS/RS and the makespan in the hybrid flowshop is established to optimize the problem. A mixed algorithm, named GA-MBO algorithm, is proposed to solve the model, which combines the advantages of the strong global optimization ability of genetic algorithm (GA) and the strong local search ability of migratory birds optimization (MBO). To avoid useless solutions, different cross operations of storage and retrieval tasks are designed. Compared with three algorithms, including improved genetic algorithm, improved particle swam optimization, and a hybrid algorithm of GA and particle swam optimization, the experimental results showed that the GA-MBO algorithm improves the operation efficiency by 9.48%, 19.54%, and 5.12% and the algorithm robustness by 35.16%, 54.42%, and 39.38%, respectively, which further verified the effectiveness of the proposed algorithm. The comparative analysis of the bi-objective experimental results fully reflects the superiority of integrated scheduling optimization. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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13 pages, 2819 KiB

Open AccessFeature PaperArticle

An Accuracy Prediction Method of the RV Reducer to Be Assembled Considering Dendritic Weighting Function

by Shousong Jin, Yanxi Chen, Yiping Shao and Yaliang Wang

Energies 2022, 15(19), 7069; https://doi.org/10.3390/en15197069 - 26 Sep 2022

Cited by 2 | Viewed by 1288

Abstract

There are many factors affecting the assembly quality of rotate vector reducer, and the assembly quality is unstable. Matching is an assembly method that can obtain high-precision products or avoid a large number of secondary rejects. Selecting suitable parts to assemble together can improve the transmission accuracy of the reducer. In the actual assembly of the reducer, the success rate of one-time selection of parts is low, and “trial and error assembly” will lead to a waste of labor, time cost, and errors accumulation. In view of this situation, a dendritic neural network prediction model based on mass production and practical engineering applications has been established. The size parameters of the parts that affected transmission error of the reducer were selected as influencing factors for input. The key performance index of reducer was transmission error as output index. After data standardization preprocessing, a quality prediction model was established to predict the transmission error. The experimental results show that the dendritic neural network model can realize the regression prediction of reducer mass and has good prediction accuracy and generalization capability. The proposed method can provide help for the selection of parts in the assembly process of the RV reducer. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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13 pages, 3201 KiB

Open AccessArticle

Energy–Carbon Emissions Nexus Causal Model towards Low-Carbon Products in Future Transport-Manufacturing Industries

by Olukorede Tijani Adenuga, Khumbulani Mpofu and Ragosebo Kgaugelo Modise

Energies 2022, 15(17), 6322; https://doi.org/10.3390/en15176322 - 30 Aug 2022

Cited by 1 | Viewed by 1123

Abstract

Climate change is progressing faster than previously envisioned. Efforts to mitigate the challenges of greenhouse gas emissions by countries through the establishment of the Intergovernmental Panel on Climate Change has resulted in continuous environmental improvements in the energy efficiency and carbon emission signatures of products. In this paper, an energy–carbon emissions nexus causal model was applied using the Leontief Input–Output mathematical model for low-carbon products in future transport-manufacturing industries., The relationship between energy savings, energy efficiency, and the carbon intensity of products for the carbon emissions signature of the future transport manufacturing in South Africa was established. The interrelationship between the variables resulted in a 29% improvement in the total energy intensity of the vehicle body part products, 7.22% in the cumulative energy savings, and 16.25% in the energy efficiency. The scope that has been examined in this paper will be interesting to agencies of government, researchers, policymakers, business owners, and practicing engineers in future transport manufacturing and could serve as a fundamental guideline for future studies in these areas. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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25 pages, 926 KiB

Open AccessArticle

An Integrated Approach-Based FMECA for Risk Assessment: Application to Offshore Wind Turbine Pitch System

by Zhen Wang, Rongxi Wang, Wei Deng and Yong Zhao

Energies 2022, 15(5), 1858; https://doi.org/10.3390/en15051858 - 03 Mar 2022

Cited by 7 | Viewed by 2682

Abstract

Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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13 pages, 1631 KiB

Open AccessArticle

An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations

by Yaping Li, Haiyan Li, Zhen Chen and Ying Zhu

Energies 2022, 15(5), 1685; https://doi.org/10.3390/en15051685 - 24 Feb 2022

Cited by 5 | Viewed by 1296

Abstract

With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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16 pages, 3762 KiB

Open AccessArticle

Fault Diagnosis Technology for Ship Electrical Power System

by Chaochun Yu, Liang Qi, Jie Sun, Chunhui Jiang, Jun Su and Wentao Shu

Energies 2022, 15(4), 1287; https://doi.org/10.3390/en15041287 - 10 Feb 2022

Cited by 10 | Viewed by 1957

Abstract

This paper proposes a fault diagnosis method for ship electrical power systems on the basis of an improved convolutional neural network (CNN) to support normal ship operation. First, according to the mathematical model of the ship electrical power system, the simulation model of the ship electrical power system is built using the MATLAB/Simulink simulation software platform in order to understand the normal working state and fault state of the generator and load in the power system. Then, the model is simulated to generate the fault response curve, and the picture dataset of the network model is obtained. Second, a CNN fault diagnosis model is designed using TensorFlow, an open-source tool for deep learning. Finally, network model training is performed, and the optimal diagnosis results of the ship electrical power system are obtained to realize structural parameter optimization and diagnosis. The diagnosis results show that the established simulation model and improved CNN can provide support for fault diagnosis of the ship electrical power system, improve the operation stability and safety of the ship electrical power system, and ensure safety of the crew. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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21 pages, 19002 KiB

Open AccessArticle

Integrated Structural Dependence and Stochastic Dependence for Opportunistic Maintenance of Wind Turbines by Considering Carbon Emissions

by Qinming Liu, Zhinan Li, Tangbin Xia, Minchih Hsieh and Jiaxiang Li

Energies 2022, 15(2), 625; https://doi.org/10.3390/en15020625 - 17 Jan 2022

Cited by 5 | Viewed by 1284

Abstract

Wind turbines have a wide range of applications as the main equipment for wind-power generation because of the rapid development of technology. It is very important to select a reasonable maintenance strategy to reduce the operation and maintenance costs of wind turbines. Traditional maintenance does not consider the environmental benefits. Thus, for the maintenance problems of wind turbines, an opportunistic maintenance strategy that considers structural correlations, random correlations, and carbon emissions is proposed. First, a Weibull distribution is used to describe the deterioration trend of wind turbine subsystems. The failure rates and reliability of wind turbines are described by the random correlations among all subsystems. Meanwhile, two improvement factors are introduced into the failure rate and carbon emission model to describe imperfect maintenance, including the working-age fallback factor and the failure rate increasing factor. Then, the total expected maintenance cost can be described as the objective function for the proposed opportunistic maintenance model, including the maintenance preparation cost, maintenance adjustment cost, shutdown loss cost, and operation cost. The maintenance preparation cost is related to the economic correlation, and the maintenance adjustment cost is described by using the maintenance probabilities under different maintenance activities. The shutdown loss cost is obtained by considering the structural correlation, and the operation cost is related to the energy consumption of wind turbines. Finally, a case study is provided to analyze the performance of the proposed model. The obtained optimal opportunistic maintenance duration can be used to interpret the structural correlation coefficient, random correlation coefficient, and sensitivity of carbon emissions. Compared with preventive maintenance, the proposed model provides better performance for the maintenance problems of wind turbines and can obtain relatively good solutions in a short computation time. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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19 pages, 10431 KiB

Open AccessArticle

A Novel Health Prognosis Method for a Power System Based on a High-Order Hidden Semi-Markov Model

by Qinming Liu, Daigao Li, Wenyi Liu, Tangbin Xia and Jiaxiang Li

Energies 2021, 14(24), 8208; https://doi.org/10.3390/en14248208 - 07 Dec 2021

Cited by 3 | Viewed by 1779

Abstract

Power system health prognosis is a key process of condition-based maintenance. For the problem of large error in the residual lifetime prognosis of a power system, a novel residual lifetime prognosis model based on a high-order hidden semi-Markov model (HOHSMM) is proposed. First, HOHSMM is developed based on the hidden semi-Markov model (HSMM). An order reduction method and a composite node mechanism of HOHSMM based on permutation are proposed. The health state transition matrix and observation matrix are improved accordingly. The high-order model is transformed into the corresponding first-order model, and more node dependency information is stored in the parameter group to be estimated. Secondly, in order to estimate the parameters and optimize the structure of the proposed model, an intelligent optimization algorithm group is used instead of the expectation–maximization (EM) algorithm. Thus, the simplification of the topology of the high-order model by the intelligent optimization algorithm can be realized. Then, the state duration variables in the high-order model are defined and deduced. The prognosis method based on polynomial fitting is used to predict the residual lifetime of the power system when the prior distribution is unknown. Finally, the intelligent optimization algorithm is used to solve the proposed model, and experiments are performed based on a set of power system data sets to evaluate the performance of the proposed model. Compared with HSMM, the proposed model has better performance on the power system health prognosis problem and can get a relatively good solution in a short computation time. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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26 pages, 6465 KiB

Open AccessArticle

Research on Machining Workshop Batch Scheduling Incorporating the Completion Time and Non-Processing Energy Consumption Considering Product Structure

by Nailiang Li and Caihong Feng

Energies 2021, 14(19), 6079; https://doi.org/10.3390/en14196079 - 24 Sep 2021

Cited by 3 | Viewed by 1231

Abstract

Energy-saving scheduling is a well-known issue in the manufacturing system. The flexibility of the workshop increases the difficulty of scheduling. In the workshop schedule, considering the collaborative optimization of multi-level structure product production and energy consumption has certain practical significance. The process sequence of parts and components should be consistent with the assembly sequence. Additionally, the non-production energy consumption (NPEC) (such as the energy consumption of workpiece handling, equipment standby, and workpiece conversion) generated by the auxiliary machining operations, which make up the majority of the total energy consumption, should not be ignored. A sub-batch priority is set according to the upper and lower coupling relationship in the product structure. A bi-objective batch scheduling model that minimizes the total energy consumption and the total completion time is developed, and the multi-objective gray wolf optimizer (MOGWO) is employed as the solution to obtain the optimal schedule scheme. A case study is performed to demonstrate the potential possibilities concerning NPEC in regard to reducing the total energy consumption and to show the effectiveness of the algorithm. Compared with the traditional optimization model, the joint optimization of NPEC and PEC can reduce the energy consumption of standby and handling by 9.95% and 22.28%, respectively. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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Review

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19 pages, 942 KiB

Open AccessReview

Operation and Maintenance Optimization for Manufacturing Systems with Energy Management

by Xiangxin An, Guojin Si, Tangbin Xia, Qinming Liu, Yaping Li and Rui Miao

Energies 2022, 15(19), 7338; https://doi.org/10.3390/en15197338 - 06 Oct 2022

Cited by 6 | Viewed by 2225

Abstract

With the increasing attention paid to sustainable development around the world, improving energy efficiency and applying effective means of energy saving have gradually received worldwide attention. As the largest energy consumers, manufacturing industries are also inevitably facing pressures on energy optimization evolution from both governments and competitors. The rational optimization of energy consumption in industrial operation activities can significantly improve the sustainability level of the company. Among these enterprise activities, operation and maintenance (O&M) of manufacturing systems are considered to have the most prospects for energy optimization. The diversity of O&M activities and system structures also expands the research space for it. However, the energy consumption optimization of manufacturing systems faces several challenges: the dynamics of manufacturing activities, the complexity of system structures, and the diverse interpretation of energy-optimization definitions. To address these issues, we review the existing O&M optimization approaches with energy management and divide them into several operation levels. This paper addresses current research development on O&M optimization with energy-management considerations from single-machine, production-line, factory, and supply-chain levels. Finally, it discusses recent research trends in O&M optimization with energy-management considerations in manufacturing systems. Full article

(This article belongs to the Special Issue Management of Energy and Manufacturing System)

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