Enabling technology for maintenance in a smart factory: A literature review

Industry 4.0 technologies are transforming the factory in an "intelligent" or "smart" factory. In a such context, a greater efficiency and innovative relationship is basically demanded within the whole production chain, including suppliers, producers, and customers. To be more competitive, companies are becoming increasingly aware that maintenance plays a key role during the digital transformation from the perspective of both technology and management. In this work, we perform a literature review of published cases to investigate how maintenance is changing through technologies of Industry 4.0 currently used in maintenance. We found 34 papers in literature involved in analyzing relations between maintenance and Industry 4.0 technology. The analysis of such studies let us to establish the current technology state-of-art and identify the most suited technology that today is employed in maintenance tasks. In particular Industrial Internet of Things and Cloud Computing are more common in the analyzed studies, confirming how these concepts and technologies are at the basis of Industry 4.0

A data-mining approach for wind turbine fault detection based on scada data analysis using artificial neural networks

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

Decarbonization of heat through low-temperature waste heat recovery: proposal of a tool for the preliminary evaluation of technologies in the industrial sector

In an industrial energy scenario increasingly focused on decarbonization and energy cost containment, waste heat is a resource that is no longer negligible. Despite the great abundance of waste heat, its recognized potential, and numerous technologies available for its use, the rate of waste heat recovery (WHR) is still low, especially at low temperatures (<230 degrees C). Non-technological barriers, such as the lack of knowledge and support tools, strongly limit the diffusion of WHR technologies. The work presented in this paper aims to overcome non-technological gaps by developing a simple and operational tool that can support companies in the preliminary stages of evaluating a WHR application. The methodology followed involved the development of specific data-based models for WHR technology sizing by correlating waste heat input characteristics with dimensional and economic parameters of the technologies evaluated. We considered the most representative technologies in the WHR scenario: organic Rankine cycles for electric power generation, heat pumps for thermal power generation, absorption chillers for cooling generation, and plate heat exchangers for low-temperature heat exchange applications. One of the significant strengths of the tool is that it was developed using real and hard-to-find technologies performance and cost data mainly collected through continuous interactions with WHR technology providers. Moreover, the interaction with the technology providers allowed contextualization and validation of the tool in the field. In addition, the tool was applied to three large companies operating in the Italian industrial sector to test its effectiveness. The tool applications made it possible to propose cost-effective solutions that the companies had not considered before, despite the high level of attention with which they were already approaching energy efficiency improvements. The result obtained demonstrates the applicability and innovativeness of the tool

Low-fiber alfalfa (Medicago sativa L.) meal in the laying hen diet: Effects on productive traits and egg quality

Abstract This study was designed to determine the effects on laying performance and egg quality resulting from partial substitution of soybean meal (SBM) with low-fiber alfalfa (LFA; Medicago sativa L.) meal in the diet of early-phase laying hens. ISA Brown layers, 18 wk of age, were randomly allocated to 2 dietary treatments and fed for 10 wk. The hens were fed 2 wheat middling–based diets: a control diet, which contained SBM (15% of diet), and a test diet containing LFA (15% of diet) as the main protein source. Low-fiber alfalfa meal was obtained by a combination of sieving and air-classification processes. Feed intake was recorded daily, and egg production was calculated on a hen-day basis; eggs from each group were weekly collected to evaluate egg components and quality. The partial substitution of SBM with LFA had no adverse effect on growth performance of early-phase laying hens. Egg production and none of the egg-quality traits examined were influenced by dietary treatment, except for yolk color (

Design of a Database of Case Studies and Technologies to Increase the Diffusion of Low-Temperature Waste Heat Recovery in the Industrial Sector

The recovery of waste heat is a fundamental means of achieving the ambitious medium- and long-term targets set by European and international directives. Despite the large availability of waste heat, especially at low temperatures (<250 degrees C), the implementation rate of heat recovery interventions is still low, mainly due to non-technical barriers. To overcome this limitation, this work aims to develop two distinct databases containing waste heat recovery case studies and technologies as a novel tool to enhance knowledge transfer in the industrial sector. Through an in-depth analysis of the scientific literature, the two databases' structures were developed, defining fields and information to collect, and then a preliminary population was performed. Both databases were validated by interacting with companies which operate in the heat recovery technology market and which are possible users of the tools. Those proposed are the first example in the literature of databases completely focused on low-temperature waste heat recovery in the industrial sector and able to provide detailed information on heat exchange and the technologies used. The tools proposed are two key elements in supporting companies in all the phases of a heat recovery intervention: from identifying waste heat to choosing the best technology to be adopted

Private hospital energy performance benchmarking using energy audit data: an Italian case study

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

Catching the flu: Syndromic surveillance, algorithmic governmentality and global health security

How do algorithms shape the imaginary and practice of security? Does their proliferation point to a shift in the political rationality of security? If so, what is the nature and extent of that shift? This article explores these questions in relation to global health security. Prompted by an epidemic of new infectious disease outbreaks – from HIV, SARS and pandemic flu, through to MERS and Ebola – many governments are making health security an integral part of their national security strategies. Algorithms are central to these developments because they underpin a number of nextgeneration syndromic surveillance systems now routinely used by governments and international organizations to rapidly detect new outbreaks globally. This article traces the origins, design and evolution of three such internet-based surveillance systems: 1) the Program for Monitoring Emerging Diseases, 2) the Global Public Health Intelligence Network, and 3) HealthMap. The article shows how the successive introduction of those three syndromic surveillance systems has propelled algorithmic technologies into the heart of global outbreak detection. This growing recourse to algorithms for the purposes of strengthening global health security, the article argues, signals a significant shift in the underlying problem, nature, and role of knowledge in contemporary security practices

Complement Activation Determines the Therapeutic Activity of Rituximab In Vivo

Rituximab is an anti-CD20 chimeric mAb effective for the treatment of B-NHL. It can lyse lymphoma cells in vitro through both C- and Ab-dependent cellular cytotoxicity. The mechanism of action of rituximab in vivo is however still unclear. We have set up a new in vivo model in nonimmunodeficient mice by stable transduction of the human CD20 cDNA in the murine lymphoma line EL4. Animals injected i.v. with the EL4-CD20+ lymphoma cells died within 30 days with evident liver, spleen, and bone marrow involvement, confirmed by immunohistochemistry and PCR analysis. A single injection of rituximab or the murine anti-CD20 Ab 1F5, given i.p. 1 day after the tumor, cured 100% of the animals. Indeed, at week 4 after tumor cell inoculation, CD20+ cells were undetectable in all organs analyzed in rituximab-treated animals, as determined by immunohistochemistry and PCR. Rituximab had no direct effect on tumor growth in vitro. Depletion of either NK cells or neutrophils or both in tumor-injected animals did not affect the therapeutic activity of the drug. Similarly, rituximab was able to eradicate tumor cells in athymic nude mice, suggesting that its activity is T cell independent. In contrast, the protective activity of rituximab or the 1F5 Ab was completely abolished in syngeneic knockout animals lacking C1q, the first component of the classical pathway of C (C1qa−/−). These data demonstrate that C activation is fundamental for rituximab therapeutic activity in vivo

A digital shadow cloud-based application to enhance quality control in manufacturing

In Industry 4.0 era, rapid changes to the global landscape of manufacturing are transforming industrial plants in increasingly more complex digital systems. One of the most impactful innovations generated in this context is the "Digital Twin", a digital copy of a physical asset, which is used to perform simulations, health predictions and life cycle management through the use of a synchronized data flow in the manufacturing plant. In this paper, an innovative approach is proposed in order to contribute to the current collection of applications of Digital Twin in manufacturing: a Digital Shadow cloud-based application to enhance quality control in the manufacturing process. In particular, the proposal comprises a Digital Shadow updated on high performance computing cloud infrastructure in order to recompute the performance prediction adopting a variation of the computer-aided engineering model shaped like the actual manufactured part. Thus, this methodology could make possible the qualification of even not compliant parts, and so shift the focus from the compliance to tolerance requirements to the compliance to usage requirements. The process is demonstrated adopting two examples: the structural assessment of the geometry of a shaft and the one of a simplified turbine blade. Moreover, the paper presents a discussion about the implications of the use of such a technology in the manufacturing context in terms of real-time implementation in a manufacturing line and lifecycle management. Copyright (C) 2020 The Authors