IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning

The assets in Industry 4.0 are categorised into physical, virtual and human. The innovation and popularisation of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc. for assets identification and tracking. The generated data from Industrial Internet of Things (IIoT) eases information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment. Predictive maintenance enables the business owner to decide such as repairing or replacing the component before an actual failure which affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimise the tasks distributions and predictive maintenance model. This paper presents the Genetic Algorithm (GA) based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost and energy performance of GA along with MinMin, MaxMin, FCFS, RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time datasets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost and energy usage. The execution time is 0.48%faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively

Smart digital twin for ZDM-based job-shop scheduling

[EN] The growing digitization of manufacturing processes is revolutionizing the production job-shop by leading it toward the Smart Manufacturing (SM) paradigm. For a process to be smart, it is necessary to combine a given blend of data technologies, information and knowledge that enable it to perceive its environment and to autonomously perform actions that maximize its success possibilities in its assigned tasks. Of all the different ways leading to this transformation, both the generation of virtual replicas of processes and applying artificial intelligence (AI) techniques provide a wide range of possibilities whose exploration is today a far from negligible sources of opportunities to increase industrial companies¿ competitiveness. As a complex manufacturing process, production order scheduling in the job-shop is a necessary scenario to act by implementing these technologies. This research work considers an initial conceptual smart digital twin (SDT) framework for scheduling job-shop orders in a zero-defect manufacturing (ZDM) environment. The SDT virtually replicates the job-shop scheduling issue to simulate it and, based on the deep reinforcement learning (DRL) methodology, trains a prescriber agent and a process monitor. This simulation and training setting will facilitate analyses, optimization, defect and failure avoidance and, in short, decision making, to improve job-shop scheduling.The research that led to these results received funding from the European Union H2020 Programme with grant agreement No. 825631 Zero-Defect Manufacturing Platform (ZDMP) and Grant agreement No. 958205 Industrial Data Services for Quality Control in Smart Manufacturing (i4Q), and from the Spanish Ministry of Science, Innovation and Universities with Grant Agreement RTI2018-101344-B-I00 "Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)"Serrano Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart digital twin for ZDM-based job-shop scheduling. IEEE. 510-515. https://doi.org/10.1109/MetroInd4.0IoT51437.2021.948847351051

Smart manufacturing scheduling: A literature review

[EN] Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified.This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0) ' (RTI2018-101344-B-I00) , the European Union H2020 research and innovation programme with grant agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP) " and the European Union H2020 research and innovation programme with agreement No. 958205 "In-dustrial Data Services for Quality Control in Smart Manufacturing (i4Q) ".Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart manufacturing scheduling: A literature review. Journal of Manufacturing Systems. 61:265-287. https://doi.org/10.1016/j.jmsy.2021.09.0112652876

Visual Management Implementation at Primetals Technologies

Our project goal was to produce a visual management system for the MMD-2 and VMC-5 machines at Primetals. We defined the relevant key process indicators (KPIs), produced a specification detailing the importance of specific KPIs and relevant data. We produced a visual design which was then modeled using python to make a functioning application. We then performed market research and a business analysis to compare alternative software for Primetals. Our MQP deliverables are: KPI specification of important KPIs, a visual design of a screen, an application mockup, market analysis of alternative software options, and a business analysis of selected companies. After conducting qualitative, quantitative and business analysis we concluded that Predator MDC is the preferred software alternative

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

Review of Open Source Simulators in ICS/IIoT Security Context

In industrial control systems (ICS), simulation has found widespread use during system design and in tuning process control parameters or exploring the effects of new control algorithms. Simulation enables the assessment of performance at scale and allows research to be conducted by those with limited access to real physical infrastructures. However, as ICSs are often no longer isolated from other networks and the internet, hence are subject to security and safety issues, simulation is also required to understand the issues and their solution. To foster transparent, collaborative and cost-effective studies, demonstrations, and solution development, and attract the broadest interest base, simulation is indeed critical and Open Source is a good way to go since simulators in this category are less expensive to access, install, and use, and can be run with general purpose (non-proprietary) computing equipment and setups. Findings This research presents the following key findings: 1. A lot of Open Source simulation tools exist and span applications areas such as communications and sensor networks (C&WSNs), ICS/SCADA, and IIoT. 2. The functional structures and characteristics that appear common in Open Source simulators include: supported licence types, programming languages, operating systems platforms, user interface types, and available documentation and types. 3. Typical research around Open Source simulators is built around modelling, analysis and optimisation of operations in relations to factors such as flexibility, mobility, scalability, and active user support. No single Open Source simulator addresses all conceivable characteristics. While some are strong in specific contexts relative to their development, they are often weak in other purpose-based research capabilities, especially in the context of IoT. 4. Most of the reviewed Open Source tools are not designed to address security contexts. The few that address security such as SCADASim only consider very limited contexts such as testing and evaluating Denial-of-Service (DoS), Man-in-the-middle (Mitm), Eavesdropping, and Spoofing attacks. Recommendations The following key recommendations are presented: 1. Future developments of Open Source simulators (especially for IIoT) should explore the potential for functionalities that can enable the integration of diverse simulators and platforms to achieve an encompassing setup. 2. Developers should explore the capabilities of generic simulators towards achieving architectures with expansible capabilities into multi-class domains, support easier and faster modelling of complex systems, and which can attract varied users and contributors. 3. Functional characteristics such as; ease of use, degree of community acceptance and use, and suitability for industrial applications, should also be considered as selection and development criteria, and to emphasise simulator effectiveness. This can support consistency, credibility, and simulation system relevance within a domain that is continually evolving. 4. Future Open Source simulation projects developments should consider and adopt the more common structural attributes including; Platform Type, Open Source Licence Type, Programming Language, User Interfaces, Documentation, and Communication Types. These should be further complemented by appropriate editorial controls spanning quality coding, revision control and effective project disseminations and management, to boost simulation tool credibility and wide acceptance. 5. The range of publication dates (earliest to latest) for: citations, code commits, and number of contributors associated to Open Source simulator projects can also support the decision for interests and adoption of specific Open Source projects. 6. Research objectives for ICS/IIoT Open Source simulators should also include security performance and optimisation with considerations towards enhancing confidentiality, integrity and availability. 7. Further studies should explore the evaluation of security topics which could be addressed by simulation – more specifically, proposing how this may be achieved and identifying what can't be addressed by simulation. Investigations into simulation frameworks that can allow multi-mode simulations to be configured and operated are also required. Research into Industry 4.0 System-of-Systems (SoS) security evaluations, dependency, and cascading impacts method or analysis is another area of importanc