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

Tedeschi, Stefano; Emmanouilidis, Christos; Farnsworth, Michael; Mehnen, Jörn; Roy, Rajkumar

doi:10.1007/978-3-319-66923-6_46

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…The introduction of maintenance services through smart retrofitting can increase the performance of manufacturing systems, optimizing their production capabilities (Tedeschi et al, 2017;Ramani et al, 2016;Jung & Jin, 2018). This is possible by improving the availability of physical assets, which is in turn a function of their reliability and maintainability (Parra & Crespo, 2015).…”

Section: Performancementioning

confidence: 99%

Smart retrofitting in maintenance: a systematic literature review

2022

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The last decade saw the rise of digitalization and data-supported decision making in the manufacturing industry: the Fourth Industrial Revolution. This trend, also known as Industry 4.0, allows manufacturing enterprises to discover manufacturing uncertainties and measure their real manufacturing capability. One of the ways in which Industry 4.0 trends have been exploited is in the improvement of maintenance, which went from following planning-focused paradigms to more proactive-focused stances. Enabling the Industry 4.0 vision for maintenance purposes has historically required companies to either replace or upgrade their existing legacy devices. It is through the latter course of action that Smart retrofitting in maintenance (SRM) intends to bring value to enterprises. This work aims to present a systematic literature review on SRM, utilizing the oft-cited PRISMA methodology. Through this analysis, a definition of SRM that reflects the current state of the art is proposed. Furthermore, the research in SRM applied in the context of different maintenance strategies is assessed (i.e. reactive, planned, proactive and strategic maintenance), and the most common drivers and challenges in SRM are presented. Finally, a roadmap for the implementation of SRM is proposed. The analysis of the SRM literature reveals that there are important research opportunities in the exploitation of SRM for strategic maintenance and asset management. The authors hope that this document leads to the consolidation of a new research area that aims to add value to maintenance in enterprises through the application of smart retrofitting in preexisting legacy devices.

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Section: Performancementioning

confidence: 99%

Smart retrofitting in maintenance: a systematic literature review

2022

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“…It demonstrated a direct connection between legacy machine input/output circuits and the data distribution lines with appropriate PLCs. Research in [15] presented a cloudbased method to enable monitoring and data collection from legacy systems. Although this method elegantly addressed security challenges in IIoT-enabled legacy machinery monitoring, the authors focused mainly on remote controllability of actuators on legacy machines.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts

Deshpande,

Telikicherla,

Jakkali

et al. 2020

Preprint

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Digitization has led to smart, connected technologies be an integral part of businesses, governments and communities. For manufacturing digitization, there has been active research and development with a focus on Cloud Manufacturing (CM) and the Industrial Internet of Things (IIoT). This work presents a computer vision toolkit (CV Toolkit) for non-invasive digitization of the factory floor in line with Industry 4.0 requirements for factory data collection. Currently, technical challenges persist towards digitization of legacy systems due to the limitation for changes in their design and sensors. This novel toolkit is developed to facilitate easy integration of legacy production machinery and factory floor artifacts with the digital and smart manufacturing environment with no requirement of any physical changes in the machines. The system developed is modular, and allows real-time monitoring of production machinery. Modularity aspect allows the incorporation of new software applications in the current framework of CV Toolkit. To allow connectivity of this toolkit with manufacturing floors in a simple, deployable and cost-effective manner, the toolkit is integrated with a known manufacturing data standard, MTConnect, to translate the digital inputs into data streams that can be read by commercial status tracking and reporting software solutions. The proposed toolkit is demonstrated using a mock-panel environment developed in house at the University of Cincinnati to highlight its usability.

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“…In their work Wang et al (2017) cite the need for further research in the development of distributed data analysis practice and co-ordination of heterogeneous data streams and for improved security for data communication. The question of enhanced security practice for cloud based CBM practice is one explored by Tedeschi et al (2017) who propose a structured approach to the assessment of security requirements within a cloud based CBM system. In more recent work Bowden et al (2019) propose a 'plug and Play' end to end cloud architecture for predictive maintenance.…”

Section: Iotmentioning

confidence: 99%

Intelligent decision support for maintenance: an overview and future trends

Turner

Emmanouilidis

Tomiyama

et al. 2019

International Journal of Computer Integrated Manufacturing

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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 a framework 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 a comprehensive framework for its processing, analysis and use should be a valuable 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 and the provision of comprehensive framework for its processing analysis and use, allowing future systems to enable 'Human in the loop' interactions.

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

Cited by 10 publications

References 16 publications

Smart retrofitting in maintenance: a systematic literature review

Smart retrofitting in maintenance: a systematic literature review

Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts

Intelligent decision support for maintenance: an overview and future trends

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