Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) ...costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm.
•Application of RCM, based on FMEA, to photovoltaic systems.•MBS and FMEA determine RPN.•New maintenance plan aimed to reduce fixed RPN of criticalities.•Lower values of RPN by FMEA and major ...reliability of system obtained.
There have been many scientific advances in the improvement of renewable energy systems. Recently, considerable interest has been given to their optimized management during their service life due to a large increase in the number of new renewable energy source power plants. High reliability levels are as important as high yields in order to maximize the useful green energy produced. Solar energy has been one of the most popular and exploited renewable sources in the market and therefore improvements in its efficiency and reliability have had a considerable impact. All energy systems require an increase in their conversion efficiency to reduce the consumption of primary energy. Moreover, the optimization of the performance of photovoltaic systems has increased their incidence as renewable sources in global power generation and has boosted their profitability. A failure of the components and sub-components of a working energy system cause two main issues; the first direct implication for the plant is the damage of the components and sub-components, and the second indirect implication is the consequent lack of energy production due to the plant being out of order. Furthermore, unforeseen failures of the components increase the uncontrollability of photovoltaic power systems, which worsens electric grid dispatching.
The work presented here provides, for the first time, a complete and new assessment of Reliability Centered Maintenance carried out using a failure mode and effect analysis approach to photovoltaic systems. We use a large volume of data derived from a database of real maintenance activities carried out by a multinational company. These data were interpreted by the opinions of experts with specialist experience in the installation, operation, and maintenance of photovoltaic power systems, from small to multi-megawatt size. The present work here has advantages over many previous studies since the information was derived from real experiences of photovoltaic systems which allowed for a more realistic risk analysis and, especially, this information was also used to revise the maintenance plan of photovoltaic installations and to optimize their effectiveness, concentrating on various failure modes which mostly affect production or which can be easily removed/reduced.
Industry 4.0 and Industry 5.0 have introduced a lot of innovative technologies in industrial plants, transforming them into complex digital systems. On the other hand, the importance of Energy ...Management Systems in industrial plants is growing for both sustainability and economic reasons, but the opportunity of Industry 4.0/5.0 technologies in enhancing energy management systems is not fully understood. Thus, this paper analyzes how Industry 4.0/5.0 technologies can be applied to meet the requirements of Energy Management Systems, focusing on each aspect such as design, monitoring, control, and budget planning. It identifies additional opportunities that arise with different levels of technological implementation, suggesting organic implementation steps. The final aim is to provide a comprehensive framework for fostering a strategic and conscious implementation approach of these tools in the Energy Management Systems of industrial plants, giving clear and comprehensive suggestions.
Promoting energy efficiency is a key element of the strategic commitment of the European Community. Prominent among the binding measures established by the 2012 Energy Efficiency Directive to further ...this vision is the requirement for large companies to conduct energy audits every four years. After receiving the second cycle of energy audit reports in December 2019, a new description of the energy situation of Italian companies was made available. This presented the previously inaccessible possibility of comparing the two situations reported in 2015 and 2019 to assess the development of energy efficiency practices in organizations subject to the legislative obligation of energy audits in the country. To this end, in collaboration with the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), a project was initiated with the aim of developing the tools and methodologies necessary to assess in more detail the evolution that has occurred in the four years since 2015. In this paper, the findings of the analysis conducted on a significant sample of companies in Italy are presented. Through the design of a maturity model to assess the degree of progress achieved in a company’s energy management, the results of the two situations were analyzed. The analysis was deepened by assessing the progress achieved in different aspects of Energy Management: “Strategic Approach”, “Awareness, Competence, and Knowledge”, “Methodological Approach”, “Organizational Structure”, “Energy Performance Management and Information System”, and “Best Practices”. Furthermore, the observed variations were statistically tested using a pairwise t-test to make statistical inferences about the maturity of the total population of Italian enterprises under legislative obligation. The results have shown an increase in overall energy management maturity in each maturity dimension.
Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to ...compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability.
The increased focus on energy efficiency, both at the national and international levels, has fostered the diffusion and development of specific energy consumption benchmarks for most relevant ...economic sectors. In this context, energy-intensive facilities, such as hospitals and health structures, represent a unique case. Indeed, despite the high energy consumption of these structures, scientific literature lacks the presence of adequate energy performance benchmarks, especially in regard to the European context. Thus, this study aimed at defining energy benchmark indicators for the Italian private healthcare sector using data collected from the Italian mandatory energy audits according to Art.8 EU Directive 27/2012. The benchmark indicators’ definition was made using a methodology proposed by the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA). This methodology provided the calculation of specific energy performance indicators (EnPIs) by considering the global energy consumption of the different sites and the sector’s relevant variables. The results obtained were compared with those obtained from a consolidated but more complex methodology: the one envisaged by the Environmental Protection Agency. The results obtained allowed us to validate the reliability of the proposed methodology, as well as the validity and future usability of the calculated indicators. Relying on a significant database containing actual data from recent energy audits, this study was thus able to provide an up-to-date and reliable benchmark for the private healthcare sector.
•This study is the first systematic review concerning maintenance in Industry 4.0.•The impact of each Industry 4.0 technology in maintenance tasks has been identified.•Remote Maintenance had an ...increase due to Augmented Reality and smart devices.•The results show Self-maintenance is an attractive possibility for smart factories.•Smart factories require changes in manufacturing and operators management.
Industry 4.0 is revolutionizing manufacturing, increasing flexibility, mass customization, quality and productivity. In today's competitive manufacturing scenario, maintenance is one of the most critical issues and companies are approaching its digital transformation from technological and management perspectives.
This article carries out a systematic literature review aimed to investigate how maintenance tasks and maintenance management strategies are changing in Industry 4.0 context, analyzing the state-of-the-art of Industry 4.0 technologies currently employed in maintenance and the resulting potential innovations in maintenance policies and manufacturing management. In addition, the most relevant trends in current maintenance policies have been investigated, such as “remote maintenance” and the attractive possibility of a “self-maintenance”. Also, the importance of human factor has been considered. The results are summarized in a comprehensive database, to provide, through concepts and empirical evidence present in literature, examples and strategies for the implementation of maintenance in Industry 4.0.
Growing environmental concerns caused by increasing consumption of naturalresources and pollution need to be addressed. Manufacturing dictates the efficiencywith which resource inputs are transformed ...into economically valuableoutputs in the form of products and services. Consequently, it is also responsiblefor the resulting waste and pollution generated from this transformation process.As a matter of fact, about one-third of the global total energy consumption isassociated withmanufacturing activities; thus, achieving higher energy efficiencyin this sector has been the focus of research as well as of policy and industrialprogrammes in recent years. In particular, being able to effectively manageenergy and energy-related activities has proved to be a fundamental capabilityfor companies willing to improve their sustainability, as it constitutes the first,critical step to understanding their processes and to identifying and correctlyevaluating improvement opportunities.This Special Issue focuses on energy management and sustainability of bothmanufacturing processes and systems, including methods, practices, tools, applicationsand experiences.
•A methodology to enable energy consumption control automation is proposed.•The methodology is based on the use of Artificial Neural Networks.•A method to control the accuracy of the model over time ...is proposed.•Two methods to enable automatic retraining of the network are proposed.•Retraining methods are evaluated on their accuracy over time.
Energy consumption control in energy intensive companies is always more considered as a critical activity to continuously improve energy performance. It undoubtedly requires a huge effort in data gathering and analysis, and the amount of these data together with the scarceness of human resources devoted to Energy Management activities who could maintain and update the analyses’ output are often the main barriers to its diffusion in companies. Advanced tools such as software based on machine learning techniques are therefore the key to overcome these barriers and allow an easy but accurate control. This type of systems is able to solve complex problems obtaining reliable results over time, but not to understand when the reliability of the results is declining (a common situation considering energy using systems, often undergoing structural changes) and to automatically adapt itself using a limited amount of training data, so that a completely automatic application is not yet available and the automatic energy consumption control using intelligent systems is still a challenge.
This paper presents a whole new approach to energy consumption control, proposing a methodology based on Artificial Neural Networks (ANNs) and aimed at creating an automatic energy consumption control system.
First of all, three different structures of neural networks are proposed and trained using a huge amount of data. Three different performance indicators are then used to identify the most suitable structure, which is implemented to create an energy consumption control tool. In addition, considering that huge amount of data are not always available in practice, a method to identify the minimum period of data collection to obtain reliable results and the maximum period of usability is described. The general purpose of the work is to allow the automatic utilization of this kind of tools, so a method to identify a lack of accuracy in the model and two different retraining methods are proposed and compared (Mobile Training and Growing Training).
The whole approach is eventually applied to the case study of a tertiary building in Rome (Italy).
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