SPOF—Slave Powerlink on FPGA for Smart Sensors and Actuators Interfacing for Industry 4.0 Applications

Valente, G.; Muttillo, Vittoriano; Muttillo, Mirco; Barile, Gianluca; Leoni, Alfiero; Tiberti, Walter; Pomante, Luigi

doi:10.3390/en12091633

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…This automation increases productivity, reduces costs, improves efficiency, and increases safety [1]. There is growing talk of Industry 4.0 [2], in which manufactured products can control their manufacturing processes. Let us recall that in the first industrial revolution it was the question of the mechanization of the industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

2019

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This paper will present a complete discussion in recent design strategies for harvesting vibration energy using piezoelectric cantilever transducers. The interest in this primary energy source is due to its presence in non-negligible quantities in most of the engines used in the industrial process. Previous work has shown that it is possible to harvest significant amounts of energy capable of supplying a wireless sensor (WS) node. However, in most research, only one step of the energy conversion and utilization chain is studied. Starting from the definition of the different design issues for a piezoelectric micro generator (PMG), the leading optimization solutions will be reviewed in this paper. Based on the findings, the quantification of the data transmission range of wireless sensor nodes powered by a PMG is proposed to support the objectives envisioned by Industry 4.0. The vibration characteristics taken from mining locomotives that have not yet been treated previously are used to illustrate the improvement of the various optimization solutions. Through our objectives, this work offers a comprehensive discussion on the use of vibrational energy by wireless sensors, bringing together the fields of mechanics, electrical, electronics, and wireless communications. The theoretical basis for each design stage is provided through the design equations. Based on actual measurements of ambient vibration, it is demonstrated, considering an optimal design of the PMG, that a WS could transmit data beyond 1 km for physical phenomena to be controlled every 7 min.Energies 2020, 13, 63 2 of 28The data collected by WS are transmitted wirelessly to BS. Given the advantages offered by the WS, namely flexibility, ease of deployment, low cost, they are now playing a vital role in the industry 4.0 [8]. More specifically, in the industrial environment, WSs are used in the automation system [9], operations control, system monitoring (humidity, temperature, fire alarm, dust index, toxic gas, and pollution) [9,10], and motion control [11]. In most of these industrial applications, WSs are placed in difficult-to-access locations (mining crushers for example) and maintenance operations for the replacement of batteries can be costly.This issue of energy autonomy of WSs has given rise in recent years to a new field of research known as energy harvesting (EH) [12]. Energy harvesting is the process of converting ambient energy into electrical energy; the most targeted application is the power supply of the WSs.EH technologies differ from one another depending on the nature of the used primary energy source. The main considered sources are the internal light, electromagnetic energy, vibration, and heat [13]. Among these primary sources, vibration abounds in the industrial applications due to the many engines used in most processes [14]. Concerning the specific case of the mining industry, crushers that operate 24 h a day, mining locomotives for the transport of personnel and minerals, trucks, are examples of sources of vibration.A lot of rese...

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Section: Introductionmentioning

confidence: 99%

Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

2019

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“…However, ongoing advancements in PLC technology, including the integration of IoT and AI, are expected to mitigate these challenges and further enhance machine monitoring systems. IoT integration allows for remote monitoring and control of industrial equipment, reducing the need for on-site personnel and lowering operational costs, while AI algorithms for predictive maintenance can reduce downtime and improve efficiency (Valente et al, 2019)…”

Section: Case Studies On Plc-based Monitoring Systemsmentioning

confidence: 99%

Untitled

2024

GMJ

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Designing a state-of-the-art PLC-based extrusion machine with a user-friendly HMI ensures seamless operation, enhancing Overall Equipment Effectiveness (OEE). This project focuses on automating an extrusion system with advanced technologies for optimized functionality and reliability. The architecture includes sophisticated components to boost productivity and product quality. Key aspects involve orderly control and synchronization of the extruder motor, feeder motor, lubrication pump, and vacuum pump for consistent performance with precise temperate profile. A significant innovation is the centralized blower system for machine temperature profile analysis and control, replacing individual controllers to enhance thermal management efficiency and ensure uniform temperature distribution. A high-low temperature alarm system alerts operators to deviations, maintaining process stability. Real-time data on current (Amps) and frequency (Hz) is displayed on the HMI from the inverter for monitoring and diagnostics. The system also features machine downline controlling capabilities for efficient management of downstream processes. Collectively, these innovations create a robust, efficient, and user-friendly extrusion machine that enhances OEE and product quality.

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“…Although GPU is originally designed for image processing, they are also widely used in data analytics since both image processing and data analytics involve intensive matrix calculations. Besides GPU, field-programmable gate arrays (FPGA) are another computing unit to speed up data analytics (Valente et al, 2019 ). FPGA is an integrated circuit that consists of internal hardware blocks with user-programmable interconnects to customize operation for a specific application.…”

Section: Related Conceptsmentioning

confidence: 99%

Data Analytics in Industry 4.0: A Survey

Duan

2021

Inf Syst Front

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Industry 4.0 is the fourth industrial revolution for decentralized production through shared facilities to achieve on-demand manufacturing and resource efficiency. It evolves from Industry 3.0 which focuses on routine operation. Data analytics is the set of techniques focus on gain actionable insight to make smart decisions from a massive amount of data. As the performance of routine operation can be improved by smart decisions and smart decisions need the support from routine operation to collect relevant data, there is an increasing amount of research effort in the merge between Industry 4.0 and data analytics. To better understand current research efforts, hot topics, and tending topics on this critical intersection, the basic concepts in Industry 4.0 and data analytics are introduced first. Then the merge between them is decomposed into three components: industry sectors, cyber-physical systems, and analytic methods. Joint research efforts on different intersections with different components are studied and discussed. Finally, a systematic literature review on the interaction between Industry 4.0 and data analytics is conducted to understand the existing research focus and trend.

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SPOF—Slave Powerlink on FPGA for Smart Sensors and Actuators Interfacing for Industry 4.0 Applications

Cited by 14 publications

References 28 publications

Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

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Data Analytics in Industry 4.0: A Survey

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