1,720,979 research outputs found
The impact of rate adaptation algorithms on wi-fi-based factory automation systems
Full text linkFactory automation systems based on the IEEE 802.11 Wi-Fi standard may benefit from its Multi-Rate Support (MRS) feature, which allows for dynamically selecting the most suitable transmission rate for the targeted application context. The MRS is implemented by means of rate adaptation algorithms (RAAs), which has already demonstrated to be effective to improve both timeliness and reliability, which are typically strict requirements of industrial real-time communication systems. Indeed, some of such algorithms have been specifically conceived for reliable real-time communications. However, the computational complexity of such algorithms has not been effectively investigated yet. In this paper, we address such an issue, particularly focusing on the execution times of some specific rate adaptation algorithms, as well as on their impact on the automation tasks. In this respect, after a formal description of the algorithms, we present the outcomes of an extensive experimental session, which includes practical measurements and realistic simulations. The obtained results are encouraging, since the measured execution times indicate that rate adaptation algorithms can be profitably adopted by industrial automation systems, allowing for improving their reliability and timeliness without impacting on the overall performance
Wi-Fi based Functional Safety: An Assessment of the Fail Safe over EtherCAT (FSoE) protocol
No full textThe introduction of the Industrial Internet of Things (IIoT) is dramatically changing the concept of manufacturing, ensuring better production flexibility, efficiency, safety and security. In this scenario, Functional Safety Networks are ever more deployed, being networks that allow to implement functional safety systems, integrated and cooperating with factory communication infrastructures that are ever more characterized by the deployment of wireless communication systems. Unfortunately, nowadays, the lack of safety protocols targeted for wireless networks represents a bottleneck in the novel smart factory development process. Thus, functional safety over wireless is becoming a hot research topic. In this paper, we address the adoption of Wi-Fi to implement functional safety networks by exploiting the black channel approach, which is at the basis of the most popular functional safety protocols designed for wired networks. In practice, with such an approach, the safety protocol is not aware of the underlying communication system. We focus on a specific protocol, namely FailSafe over EtherCAT (FSoE) and investigate its behavior over Wi-Fi. To this aim, we developed an experimental set-up and conducted several tests to adequately assess safety, reliability and timing performance. Specifically, we addressed the achievable Safety Integrated Level (SIL), the number of network re-initializations and the message delivery times. The analysis provided encouraging results and revealed different behaviors concerned with the use of different transport layer protocols (TCP and UDP) that suggest interesting future activities
IEC 61850 on 5G Communication Infrastructure: Feasibility Analysis and Practical Considerations
No full textIn recent years, electrical substations have been rapidly moving towards a fully digital paradigm. In this context, the IEC 61850 series of standards defines the communication infrastructure that allows full interoperability between different instruments and control actions, and in this paper we specifically consider the case of Phasor Measurement Units (PMUs). Unfortunately, distances between substations are a limiting factor for the adoption of high performance wired communication standards (i.e. Ethernet, TSN, etc.) due to the significant wiring costs. Therefore, we investigate here the possibility of transmitting their measurements using a 5G communication infrastructure. For this analysis, we reproduce a real distribution network and simulate plausible transmissions between several PMUs and a Phasor Data Concentrator (PDC). An average communication delay of 10 ms was observed. Based on the results obtained, it is reasonable to say that the 5G communication infrastructure may represent a valuable solution, especially in distribution networks where line lengths are reduced
Artificial Intelligence - Based Measurement Systems for Automotive: a Comprehensive Review
No full textThe development of ever intelligent systems in various application fields is nowadays a hot research topic. Artificial Intelligence (AI) techniques become a key enabler of the transition between classical static, hard-coded algorithms and innovative, flexible ones. Actually, the automotive sector can undoubtedly benefit from the usage of the aforementioned techniques, aiming at building a novel smart automotive industry. Indeed, the application of AI spreads all round the automotive sector, ranging from on-board measuring systems to customer satisfaction analysis and demand prediction. This paper aims to review the possible applications of Artificial Intelligence techniques to the automotive sector, with a special focus on innovative measurement systems and metrology. Indeed the focus will be, between others, on Advanced Driver Assistance Systems (ADAS), in-vehicle IoT systems and intelligent industrial measuring systems, thus allowing to both increase road safety and design accurate predictive maintenance, additive manufacturing systems and, in substance, to build the smart automotive factory of the future
On the Use of an Hyperspectral Imaging Vision Based Measurement System and Machine Learning for Iris Pigmentation Grading
No full textNowadays, the ability to derive accurate measurements from images, i.e. the application of vision-based systems to the measurement field, is becoming an attractive research field. In this context, Machine Learning (ML) algorithms can be exploited to smartly and automatically perform the measurement activity. This paper presents an interesting application of ML techniques to an Hyperspectral Imaging System, devoted to the analysis of the iris pigmentation. Indeed, it is proven that the iris pattern evaluation gives a chance for the analysis of both possible loss of sight and future outbreak of several eye diseases. The proposed Vision-Based Measurement system (VBM) allows to illuminate the subject eyes in the spectral range 480 - 900 nm. In particular, the imaging system foresees to take 22 different images of 2048 x 1536 pixels, thus obtaining a spectral resolution of 20 nm and a spatial resolution of 10.7 μm. In this paper, as a first research step, we evaluate the possibility to develop a suitable Machine Learning algorithm to classify the iris color. In particular, the goal is to point out the possible ML techniques that can be employed, the needed dataset and the possible advantages offered by the hyperspectral approach, compared to the conventional visible light imaging
Rate Adaptation by Reinforcement Learning for Wi-Fi Industrial Networks
No full textWireless technologies play a key role in the Industrial Internet of Things (IIoT) scenario, for the development of increasingly flexible and interconnected factory systems. Wi-Fi remains particularly attracting due to its pervasiveness and high achievable data rates. Furthermore, its Rate Adaptation (RA) capabilities make it suitable to the harsh industrial environments, provided that specifically designed RA algorithms are deployed. To this aim, this paper proposes to exploit Reinforcement Learning (RL) techniques to design an industry-specific RA algorithm. The RL is spreading in many fields since it allows to design intelligent systems by means of a stochastic discrete-time system based approach. In this work we propose to enhance the Robust Rate Adaptation Algorithm (RRAA) by means of a RL approach. The preliminary assessment of the designed RA algorithm is carried out through meaningful OMNeT++ simulations, that allow to recognize the beneficial impact of the introduction of RL with respect to several industry-specific performance indicators
A learning model for battery lifetime prediction of LoRa sensors in additive manufacturing
Full text linkToday, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low-cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments
Smart Measurement Systems Exploiting Adaptive LoRaWAN Under Power Consumption Constraints: a RL Approach
No full textA Profinet Simulator for the Digital Twin of Networked Electrical Drive Systems
No full textModern industrial manufacturing plants, especially those using coordinated electrical drives with strict timing requirements, make extensive use of real-time communication networks. These systems, typically, are based on various topologies, include diverse protocols, and connect devices from different manufacturers, which may make them difficult to study, plan and optimize. As a solution, the adoption of digital twins allows to simulate such systems under various operating conditions in a low-cost and zero-risk environment. In this paper we address the digital twin of a networked electrical drive system, focusing on the real-time communication network used to connect the drives. In particular, we describe the simulation model of Profinet IO RT Class 1, implemented as an extension of the INET library of OMNeT++. Moreover, we present the outcomes of the tests carried out on a prototype simulated network and compare them with those of the equivalent real one
A Profinet Simulator for the Digital Twin of Networked Electrical Drive Systems
Full text linkModern industrial manufacturing plants, especially those using coordinated electrical drives with strict timing requirements, make extensive use of real-time communication networks. These systems, typically, are based on various topologies, include diverse protocols, and connect devices from different manufacturers, which may make them difficult to study, plan and optimize. As a solution, the adoption of digital twins allows to simulate such systems under various operating conditions in a low-cost and zero-risk environment. In this paper we address the digital twin of a networked electrical drive system, focusing on the real-time communication network used to connect the drives. In particular, we describe the simulation model of Profinet IO RT Class 1, implemented as an extension of the INET library of OMNeT++. Moreover, we present the outcomes of the tests carried out on a prototype simulated network and compare them with those of the equivalent real one
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