A Low-Cost Environmental Monitoring System: How to Prevent Systematic Errors in the Design Phase through the Combined Use of Additive Manufacturing and Thermographic Techniques

Salamone, Francesco; Danza, Ludovico; Meroni, Italo; Pollastro, M. Cristina

doi:10.3390/s17040828

Cited by 49 publications

(34 citation statements)

References 33 publications

(44 reference statements)

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“…Based on the experimental results, the proposed system is robust enough, able to accurately measure the environmental condition in the factory in real-time, and is expected to help managers maintain an optimum working environment for the workers inside the factory. Salamone et al proposed an environmental monitoring system based on low-cost IoT sensors for preventing errors during the design phase in additive manufacturing [ 12 ]. The sensors were used to gather temperature and humidity data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recent technologies such as Internet of Things (IoT)-based sensors can be utilized and integrated with monitoring systems. Studies have been conducted in the manufacturing industry and showed significant benefits from the use of IoT-based sensors for monitoring such as working condition improvements [ 11 ], error design prevention [ 12 ], fault diagnosis [ 13 ], quality prediction [ 14 ], and helping managers with better decision making [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

262

103

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With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

262

103

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“…The nearable system is described in two papers [6,7]. It's named "nano Environmental Monitoring System" (nEMoS) but, actually, nEMoS could stands for nearable Environmental Monitoring System.…”

Section: Monitoring Systemsmentioning

confidence: 99%

An Integrated Framework for Users’ Well-Being

Salamone

Meroni

Belussi

et al. 2017

The 4th International Electronic Conference on Sensors and Applications

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Abstract:The hygro-thermal comfort (ICQ) is defined as the psychophysical state in which the subject expresses a condition of well-being with respect to environmental variables, a condition known as thermal neutrality. Furthermore, the ICQ represents one pillar of the holistic concept of the Indoor Environmental Quality (IEQ). The methods for the assessment of ICQ and recognized at international level are mainly two. The former, based on a steady-state approach, described by the EN ISO 7730:2005 and applied to Fully Mechanically Controlled buildings (FMC) equipped with an active conditioning system. The latter, based on an adaptive approach, as defined trough in field activities and described by the technical standard ASHRAE 55 and EN 15,251, instead, considers the users as active subjects that interact with surrounding environment and are influenced in their comfort perception by external conditions. In this case, the thermal comfort concept is not just defined depending on physical, but also psychological, social, economic and cultural aspects. The technical standards provides that this method could be applied in middle seasons when the control of comfort is handled by passive technological methods, i.e., in the so called Natural Ventilated or Free Running buildings (FR). In this approach, methodologies providing the direct involvement of the end user are consolidating, through the collection of physiological, psychological and behavioral personal data as to obtain the better assessment of the comfort conditions. Placing in this field, the article describes the results of a field investigation in an office aimed at defining a framework for the assessment of the thermal comfort based on the two approaches through the use of low cost technology solutions, parametric and freeware models.

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“…Among the several available technologies, those related to control and management of the energy flows within buildings provide good results in terms of diagnosis and optimization of building performance (Zong et al, 2016). This aim is achieved combining building simulation techniques, semantic data models (Terkaj et al, 2014;Gagliardo et al, 2015) and automation processes also through the use of open source hardware for control Salamone et al, 2017) or monitoring purposes . In general, a traditional Energy Management Control System (EMCS) can save between 5% and 15% of energy consumption of buildings (Dong & Lam, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of model predictive control for the optimization of thermo-hygrometric comfort and energy consumption of buildings

Danza¹,

Belussi²,

Floreani³

et al. 2018

isi

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The use of tools of simulation in every field of engineering is in the last years widely spreading. Lot of them can be used and a large amount of simulators can be found on the market in order to perform every kind of analysis and prediction. In the field of building/plant system, tools based on white, grey and black box approaches are often used as a function of accuracy and reliability. Several tools were developed according to mathematical models and transient analysis in order to perform Building Energy Simulations. The lumped capacitance models have a potential in terms of both data reliability and low computational cost. The Resistance-Capacitance models can be realized with different orders to improve the dynamic thermal behavior of building and coupled with model-based design tools. Dymola with Modelica language can provide a useful tool for engineers to design a thermohygrometric comfort model optimizing the energy consumptions. The paper describes a calculation method developed with the aid of an outdoor test cell, based on a second order Lumped parameters model coupled with a hygrometric model and a Model Predictive Control thanks to a library for real time control and management of energy consumptions and thermal comfort. RÉSUMÉ. L'utilisation d'outils de simulation dans tous les domaines de l'ingé nierie s'estlargement ré pandue ces derniè res anné es. Beaucoup d'entre eux peuvent ê tre utilisé s et une grande quantité de simulateurs peuvent ê tre trouvé s sur le marché afin d'effectuer tout type d'analyse et de pré vision. Dans le domaine des systè mes de bâtiments / installations, les outils basé s sur les approches de boî te blanche, grise et noire sont souvent utilisé s en fonction de la pré cision et de la fiabilité . KEYWORDS: building energy simulations, model predictive control, lumped parameters model, dymola. MOTS-CLÉS: simulations d'é nergie du bâtiment, commande pré dictive, modè le à paramè tre concentré e, dymola.

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A Low-Cost Environmental Monitoring System: How to Prevent Systematic Errors in the Design Phase through the Combined Use of Additive Manufacturing and Thermographic Techniques

Cited by 49 publications

References 33 publications

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

An Integrated Framework for Users’ Well-Being

Application of model predictive control for the optimization of thermo-hygrometric comfort and energy consumption of buildings

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