IoT-based monitoring of environmental conditions to improve the production performance

Mörth, Oliver; Eder, Matthias; Holzegger, Lukas; Ramsauer, Christian

doi:10.1016/j.promfg.2020.04.018

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…The technology of IoT is reliable, and it is safe in respect of health and safety measures. The application of IoT is very effective, and can provide vital information to devices [14]. Lu [15] and Mosekilde and Laugesen [16] reported that the system dynamics (SD) model could be used in a brewery, to investigate the nonlinear behavior of the beer production and distribution system and to solve supply chain problems, such as a comparative analysis of the inventory and determining the direction of the supply chain management optimisation-acceleration flow.…”

Section: Ict Connectivitymentioning

confidence: 99%

System Dynamics Modelling and Simulation of Computer Control Systems for Steel Ingot Furnaces

Somo,

Daniyan,

Swanepoel

et al. 2023

SAJIE

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The problem that necessitated this study was that some furnaces that were responsible for the further processing of steel pipes at Company X were not connected to a programmable logic control (PLC)–Internet of Things (IoT) system. This slowed down the product flow. To address this problem, PLC-IoT systems were integrated so that the production system could be more effective. This study therefore models and simulates the integrated PLC-IoT control system, using the system dynamics modelling approach to investigate the performance of the automated gas furnaces. The PLC-IoT consists of an internal module that is used to process the data stream. After the implementation of the proposed control system, primary data was acquired by continuously evaluating the performance of the system. The research methodology used involved quantitative analysis and a simulation model using the Anylogic software. The result of the delivery and inventory of ingots from an automated system showed an upward trend with delivery and an inventory of an ingot at about 90 minutes; while a system that was not automated with PLC-IoT showed a downward trend of ingots. This shows that the introduction of the PLC-IoT for control makes the system more effective. The findings of this work could assist the manufacturers in achieving manufacturing or production efficiency, significant time savings, and better monitoring of the manufacturing process. Keywords: Automation, Control system, Gas furnace, PLC-IoT, Simulation

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Section: Ict Connectivitymentioning

confidence: 99%

System Dynamics Modelling and Simulation of Computer Control Systems for Steel Ingot Furnaces

Somo,

Daniyan,

Swanepoel

et al. 2023

SAJIE

View full text Add to dashboard Cite

show abstract

“…By digitizing the physical realm, the Internet of Things (IoT) technology enables substantial societal benefits and magnifies the scope of real-time monitoring systems. Recently, the wide adoption of IoTenabled applications has amplified the explosion of real-time monitoring systems for different domains including home automation [1], energy management [2], environmental monitoring [3], production [4], security [5], and healthcare industry [6]. The extensive collection of smart expedients and applications is called the Internet of Things.…”

Section: Introductionmentioning

confidence: 99%

Design of Online Vitals Monitor by Integrating Big Data and IoT

Banu¹,

Rajamani²

2023

Computer Systems Science and Engineering

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In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.

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“…On the other hand, the IoT paradigm demands new metrics for performance evaluation since the devices and systems implemented are generally different and more complex than other traditional or more studied technologies [12]. Numerous works in the related literature present IoT monitoring platforms [13][14][15]. However, no matter the field, the Key Performance Indicators (KPI) chosen to evaluate the performance are usually different.…”

Section: Introductionmentioning

confidence: 99%

A novel system to control and forecast QoX performance in IoT‐based monitoring platforms

Martinez‐Caro,

Tasic,

Cano

2023

IET Wireless Sensor Systems

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Communication architectures based on the Internet of Things (IoT) are increasingly frequent. Commonly, these solutions are used to carry out control and monitoring activities. It is easy to find cases for manufacturing, prediction maintenance, Smart Cities, etc., where sensors are deployed to capture data that is sent to the cloud through edge devices or gateways. Then that data is processed to provide useful information and perform additional actions if required. As crucial as deploying these monitoring solutions is to verify their operation. In this article, we propose a novel warning method to monitor the performance of IoT‐based systems. The proposal is based on a holistic quality model called Quality of X (QoX). QoX refers to the use of a variety of metrics to measure system performance at different quality dimensions. These quality dimensions are data (Quality of Data, QoD), information (Quality of Information, QoI), users' experience (Quality of user Experience, QoE), and cost (Quality Cost, QC). In addition to showing the IoT system performance in terms of QoX in real‐time, our proposal includes (i) a forecasting model for independent estimation of QoX applying Deep Learning (DL), specifically using a Long Short‐Term Memory (LSTM) and time series, and (ii) the warning system. In light of our results, our proposal shows a better capacity to forecast quality drops in the IoT‐based monitoring system than other solutions from the related literature.

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IoT-based monitoring of environmental conditions to improve the production performance

Cited by 13 publications

References 16 publications

System Dynamics Modelling and Simulation of Computer Control Systems for Steel Ingot Furnaces

System Dynamics Modelling and Simulation of Computer Control Systems for Steel Ingot Furnaces

Design of Online Vitals Monitor by Integrating Big Data and IoT

A novel system to control and forecast QoX performance in IoT‐based monitoring platforms

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