New Threats for Old Manufacturing Problems: Secure IoT-Enabled Monitoring of Legacy Production Machinery

The digitization of manufacturing through the introduction of Industrie 4.0 technologies creates additional business opportunities and technical challenges. The integration of such technologies on legacy production machinery can upgrade them to become part of the digital and smart manufacturing environment. A typical example is that of industrial monitoring and maintenance, which can benefit from internet of things (IoT) solutions. This paper presents the development of an-IoT-enabled monitoring solution for machine tools as part of a remote maintenance approach. While the technical challenges pertaining to the development and integration of such solutions in a manufacturing environment have been the subject of relevant research in the literature, the corresponding new security challenges arising from the introduction of such technologies have not received equal attention. Failure to adequately handle such issues is a key barrier to the adoption of such solutions by industry. This paper aims to assess and classify the security aspects of integrating IoT technology with monitoring systems in manufacturing environments and propose a systematic view of relevant vulnerabilities and threats by taking an IoT architecture point of view. Our analysis has led to proposing a novel modular approach for secure IoT-enabled monitoring for legacy production machinery. The introduced approach is implemented on a case study of machine tool monitoring, highlighting key findings and issues for further research

A digital maintenance practice framework for circular production of automotive parts

The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered, not just for the reduction of its own direct impact but also for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research acts as a primer for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose

Circular production and maintenance of automotive parts: an Internet of Things (IoT) data framework and practice review

The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research sets out an agenda and framework for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose

Case Study based on Inequality Indices for the Assessments of Industrial Fleets

3rd IFAC Workshop on Advanced Maintenance Engineering, Services and Technology AMEST 2016: Biarritz, France, 19—21 October 2016. - IFAC-PapersOnLine, Volume 49, Issue 28, 2016, Pages 250-255This contribution illustrates the advantages of measures and indicators based on the notions of Shannon entropy which is widely implemented in thermodynamics, information theory, econometrics, and biology. The presented case study applies these indicators to a fleet of industrial assets, introducing innovatively the entropy concept to the field of availability. There is no reason to deal the effect of performance variables in assets independently without taking into account disparities in terms of reliability, operation hours, applied maintenance, operating conditions, and usage profiles, etc. Therefore, the characteristic of the method and their independence from the heterogeneity of the sample that compares is intended to be promising for applications in availability analysis. In other words, this research presents a derivation from well-known concepts such as the Gini, Hoover and Theil indices illustrating their application by the support of an example where attributes for different groups of assets are compared. To illustrate the application we are using two relevant and innovative scenarios as case study examples

A Multiclassifier Approach for Drill Wear Prediction

Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each algorithm individually and combining them according to three different methods: confidence voting, weighted voting and majority voting. To illustrate its applicability in a real problem, the drill wear detection in machine-tool sector is addressed. In this study, the accuracy obtained by each isolated classifier is compared with the performance of the multiclassifier when characterizing the patterns of interest involved in the drilling process and predicting the drill wear. Experimental results show that, in general, false positives obtained by the classifiers can be slightly reduced by using the multiclassifier approach

Generalised network architectures for environmental sensing: case studies for a digitally enabled environment

A digitally enabled environment is a setting which incorporates sensors coupled with reporting and analytics tools for understanding, observing or managing that environment. Large scale data collection and analysis are a part of the emerging digitally enabled approach for the characterisation and understanding of our environment. It is recognised as offering an effective methodology for addressing a range of complex and interrelated social, economic and environmental concerns. The development and construction of the approach requires advances in analytics control linked with a clear definition of the issues pertaining to the interaction between elements of these systems. This paper presents an analysis of selected issues in the field of analytics control. It also discusses areas of progress, and areas in need of further investigation as sensing networks evolve. Three case studies are described to illustrate these points. The first is a physical analytics test kit developed as a part of the “Reinvent the Toilet Challenge” (RTTC) for process control in a range of environments. The second case study is the Cranfield Urban Observatory that builds on elements of the RTTC and is designed to allow users to develop user interfaces to monitor, characterise and compare a variety of environmental and infrastructure systems plus behaviours (e.g., water distribution, power grids). The third is the Data and Analytics Facility for National Infrastructure, a cloud-based high-performance computing cluster, developed to receive, store and present such data to advanced analytical and visualisation tools.Engineering and Physical Sciences Research Council (EPSRC): EP/P016782/1, EP/R013411/1, EP/R012202/1 and EP/R017727/1. Bill & Melinda Gates Foundatio

Mobile Learning Applications: Exploring Location Sensing Mechanisms

In this paper, we present an exploration of two location sensing mechanisms, such as QR codes and beacons, used in Location-based Learning Applications which are performed inside small indoor spaces (e.g. a classroom). The exploration of QR codes was carried out through an implementation of a Location-based Learning Application which was used by students between 7 and 11 years old inside of Primary Schools. Meanwhile, for the exploration of beacons, a functional prototype was made which was systematically tested by students of Faculty of Informatics of UNLP (Argentina). We analysed those characteristics that we have been learned related to how these mechanisms behave when they are used inside small indoor spaces because, in these cases, relevant places to provide learning contents are nearest from each other. We wish to create a discussion about the exploration of these two location sensing mechanisms. We expect that this discussion helps not only to design and develop Location-based Learning Applications, but also to improve authoring tools that are using to generate this kind of applications.Publicado en: Agredo-Delgado V., Ruiz P. (eds) Human-Computer Interaction. HCI-COLLAB 2018. Communications in Computer and Information Science, vol 847. Springer, Cha

Boerhaave syndrome as a complication of colonoscopy preparation: a case report

<p>Abstract</p> <p>Introduction</p> <p>Colonoscopy is one of the most frequently performed elective and invasive diagnostic interventions. For every colonoscopy, complete colon preparation is mandatory to provide the best possible endoluminal visibility; for example, the patient has to drink a great volume of a non-resorbable solution to flush out all feces. Despite the known possible nauseating side effects of colonoscopy preparation and despite the knowledge that excessive vomiting can cause rupture of the distal esophagus (Boerhaave syndrome), which is a rare but severe complication with high morbidity and mortality, it is not yet a standard procedure to provide a patient with an anti-emetic medication during a colon preparation process. This is the first report of Boerhaave syndrome induced by colonoscopy preparation, and this case strongly suggests that the prospect of being at risk of a severe complication connected with an elective colonoscopy justifies a non-invasive, inexpensive yet effective precaution such as an anti-emetic co-medication during the colonoscopy preparation process.</p> <p>Case presentation</p> <p>A 73-year-old Caucasian woman was scheduled to undergo elective colonoscopy. For the colonoscopy preparation at home she received commercially available bags containing soluble polyethylene glycol powder. No anti-emetic medication was prescribed. After drinking the prepared solution she had to vomit excessively and experienced a sudden and intense pain in her back. An immediate computed tomography (CT) scan revealed a rupture of the distal esophagus (Boerhaave syndrome). After initial conservative treatment by endoluminal sponge vacuum therapy, she was taken to the operating theatre and the longitudinal esophageal rupture was closed by direct suture and gastric fundoplication (Nissen procedure). She recovered completely and was discharged three weeks after the initial event.</p> <p>Conclusions</p> <p>To the best of our knowledge, this is the first report of a case of Boerhaave syndrome as a complication of excessive vomiting caused by colonoscopy preparation. The case suggests that patients who are prepared for a colonoscopy by drinking large volumes of fluid should routinely receive an anti-emetic medication during the preparation process, especially when they have a tendency to nausea and vomiting.</p

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions