Intelligent Sensors for Real-Time Decision-Making

Coito, Tiago; Firme, Bernardo; Martins, Miguel S.E.; Vieira, Susana M.; Figueiredo, João; Sousa, J.M.

doi:10.3390/automation2020004

Cited by 22 publications

(9 citation statements)

References 62 publications

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“…Manufacturing is expected to compile vast amounts of process and product data in the near future [8,27]. Consequently, to enable automation and autonomous decision-making, we have to deal with associated big-data challenges [28,29] that are imminent due to virtually infinite volumes of available sensor data and the increased need for high-frequency sensing [1,2]. In that regard, efficiently utilizing existing computing and network infrastructure could be a key driver [30,31].…”

Section: Motivation and Potentialsmentioning

confidence: 99%

“…Manufacturing is expected to significantly benefit from recent advances regarding the Internet of Things (IoT) and Cyber-Physical Systems (CPS). Particular development directions include establishing highly dynamic business relations and creating interconnected and automated production environments, even for short-lived collaborations, through increasing degrees of automation based on (sensor) data [1,2]. Beyond promising better product quality and associated costs-usually, two conflicting goals-such advanced and automated production environments also embrace improved "soft" factor properties that have an increased influence on decision-making, especially environmental (sustainability), social, and governance criteria [3].…”

Section: Introductionmentioning

confidence: 99%

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The Road to Accountable and Dependable Manufacturing

et al. 2021

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The Internet of Things provides manufacturing with rich data for increased automation. Beyond company-internal data exploitation, the sharing of product and manufacturing process data along and across supply chains enables more efficient production flows and product lifecycle management. Even more, data-based automation facilitates short-lived ad hoc collaborations, realizing highly dynamic business relationships for sustainable exploitation of production resources and capacities. However, the sharing and use of business data across manufacturers and with end customers add requirements on data accountability, verifiability, and reliability and needs to consider security and privacy demands. While research has already identified blockchain technology as a key technology to address these challenges, current solutions mainly evolve around logistics or focus on established business relationships instead of automated but highly dynamic collaborations that cannot draw upon long-term trust relationships. We identify three open research areas on the road to such a truly accountable and dependable manufacturing enabled by blockchain technology: blockchain-inherent challenges, scenario-driven challenges, and socio-economic challenges. Especially tackling the scenario-driven challenges, we discuss requirements and options for realizing a blockchain-based trustworthy information store and outline its use for automation to achieve a reliable sharing of product information, efficient and dependable collaboration, and dynamic distributed markets without requiring established long-term trust.

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Section: Motivation and Potentialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Road to Accountable and Dependable Manufacturing

et al. 2021

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“…The DMS collects and analyzes data through a SCADA (supervisory control and data acquisition) system, which interfaces with PLCs (programmable logic controllers) installed on multiple bucket wheel excavators, belt conveyors, spreaders, and stackers in the field [25]. Similar control systems paired with OPC (Open Platform Communications) tools in a SCADA-PLC-OPC interface are commonly used for advanced monitoring of industrial processes [26][27][28][29][30]. Geotechnical monitoring applications are typically heavily supported by DMSs as well.…”

Section: Autonomous Vehicles For Mining Applicationsmentioning

confidence: 99%

Data Management System for a Semiautonomous Shuttle Car for Underground Room and Pillar Coal Mines

et al. 2021

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In recent years, autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic. This is a result of the increasing demands of society on mineral resources along with the accelerating exploitation of the currently economically viable resources, which lead the mining sector to turn to deeper, more-difficult-to-mine orebodies. An appropriate data management system comprises a crucial aspect of the designing and the engineering of a system that involves autonomous or semiautonomous vehicles. The vast volume of data collected from onboard sensors, as well as from a potential IoT network dispersed around a smart mine, necessitates the development of a reliable data management strategy. Ideally, this strategy will allow for fast and asynchronous access to the data for real-time processing and decision-making purposes as well as for visualization through a corresponding human–machine interface. The proposed system has been developed for autonomous navigation of a coalmine shuttle car and has been implemented on a 1/6th scale shuttle car in a mock mine. It comprises three separate nodes, namely, a data collection node, a data management node, and a data processing and visualization node. This approach was dictated by the large amount of collected data and the need to ensure uninterrupted and fast data management and flow. The implementation of an SQL database server allows for asynchronous, real-time, and reliable data management, including data storage and retrieval. On the other hand, this approach introduces latencies between the data management node and the other two nodes. In general, these latencies include sensor latencies, network latencies, and processing latencies. However, the data processing and visualization module is able to retrieve and process the latest data and make a decision about the next optimal movement of the shuttle car prototype in less than 900 ms. This allows the prototype to navigate efficiently around the pillars without interruptions.

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“…Among the challenges to deploying flexible manufacturing systems are the scheduling operations and the efficient use of the resources [11]. To make real-time scheduling decisions, the digital twin must be coupled with a decision support system that receives information from the real asset, using sensor technologies and business data to make scheduling decisions [12]. Scheduling in this context has the goal of assigning a set of jobs.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin of a Flexible Manufacturing System for Solutions Preparation

et al. 2022

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In the last few decades, there has been a growing necessity for systems that handle market changes and personalized customer needs with near mass production efficiency, defined as the new mass customization paradigm. The Industry 5.0 vision further enhances the human-centricity aspect, in the necessity for manufacturing systems to cooperate with workers, taking advantage of their problem-solving capabilities, creativity, and expertise of the manufacturing process. A solution is to develop a flexible manufacturing system capable of handling different customer requests and real-time decisions from operators. This paper tackles these aspects by proposing a digital twin of a robotic system for solution preparation capable of making real-time scheduling decisions and forecasts using a simulation model while allowing human interventions. A discrete event simulation model was used to forecast possible system improvements. The simulation handles real-time scheduling considering the possibility of adding identical parallel machines. Results show that processing multiple jobs simultaneously with more than one machine on critical processes, increasing the robot speed, and using heuristics that emphasize the shortest transportation time can reduce the overall completion time by 82%. The simulation model has an animated visualization window for a deeper understanding of the system.

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Intelligent Sensors for Real-Time Decision-Making

Cited by 22 publications

References 62 publications

The Road to Accountable and Dependable Manufacturing

The Road to Accountable and Dependable Manufacturing

Data Management System for a Semiautonomous Shuttle Car for Underground Room and Pillar Coal Mines

Digital Twin of a Flexible Manufacturing System for Solutions Preparation

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