An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and low-cost big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an Electric Vehicle (EV) battery module smart assembly automation system designed by the Automation Systems Group (ASG) at the University of Warwick, UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

An end-to-end big data analytics platform for IoT-enabled smart factories : a case study of battery module assembly system for electric vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

This article delineates the design and deployment of an innovative real-time water quality monitoring system tailored for rural regions, focusing on monitoring the water resource quality parameters. We propose a solar-powered, waterproof, portable, and Internet of Things (IoT)-enabled solution that leverages Long Range Wide Area Network (LoRaWAN) technology. Central to this system is a sophisticated LoRa node outfitted with an array of sensors for capturing key water parameters, such as pH, total dissolved solids, turbidity and temperature. A conjunction of an Arduino microcontroller-based board and a LoRa shield facilitates real-time data capture and transmission to a LoRaWAN gateway. The acquired data is transmitted to The Things Network server, which is seamlessly integrated with a ThingSpeak web-based IoT server and ThingView mobile applications. We incorporate a solar cell with a solar shield to ensure sustainable energy provision for powering the entire system through a rechargeable battery. This allows users to access vital water quality information online simultaneously and continuously in real-time. As a testament to its robustness, the system was empirically tested at Gambang Lake to demonstrate its effectiveness, functionality, buoyancy, and waterproof capabilities. We further validated the results by comparing them with laboratory sample analysis findings. Experimental evaluations confirmed the system's reliability, as evidenced by the strong agreement between the water conditions measured using our solution and those obtained from laboratory instruments. Moreover, our system efficiently and remotely updated data across multiple IoT platforms using the LoRa radio interface over the LoRaWAN gateway

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

This article delineates the design and deployment of an innovative real-time water quality monitoring system tailored for rural regions, focusing on monitoring the water resource quality parameters. We propose a solar-powered, waterproof, portable, and Internet of Things (IoT)-enabled solution that leverages Long Range Wide Area Network (LoRaWAN) technology. Central to this system is a sophisticated LoRa node outfitted with an array of sensors for capturing key water parameters, such as pH, total dissolved solids, turbidity and temperature. A conjunction of an Arduino microcontroller-based board and a LoRa shield facilitates real-time data capture and transmission to a LoRaWAN gateway. The acquired data is transmitted to The Things Network server, which is seamlessly integrated with a ThingSpeak web-based IoT server and ThingView mobile applications. We incorporate a solar cell with a solar shield to ensure sustainable energy provision for powering the entire system through a rechargeable battery. This allows users to access vital water quality information online simultaneously and continuously in real-time. As a testament to its robustness, the system was empirically tested at Gambang Lake to demonstrate its effectiveness, functionality, buoyancy, and waterproof capabilities. We further validated the results by comparing them with laboratory sample analysis findings. Experimental evaluations confirmed the system's reliability, as evidenced by the strong agreement between the water conditions measured using our solution and those obtained from laboratory instruments. Moreover, our system efficiently and remotely updated data across multiple IoT platforms using the LoRa radio interface over the LoRaWAN gateway

Contributions to the energy management of industrial refrigeration systems: a data-driven perspective

Nowadays, energy management has gained attention due to the constant increment of energy consumption in industry and the pollution problems that this fact supposes. On this subject, one of the main industrial sectors, the food and beverage, attributes a great percentage of its energy expenditure to the refrigeration systems. Such systems are highly affected by operation conditions and are commonly composed by different machines that are continually interacting. These particularities difficult the successful application of efficient energy management methodologies requiring further research efforts in order to improve the current approaches. In this regard, with the current framework of the Industry 4.0, the manufacturing industry is moving towards a complete digitalization of its process information. Is in this context, where the promising capabilities of the data-driven techniques can be applied to energy management. Such technology can push forward the energy management to new horizons, since these techniques take advantage of the common data acquired in the refrigeration systems for its inner operation to develop new methodologies able to reach higher efficiencies. Accordingly, this thesis focuses its attention on the research of novel energy management methodologies applied to refrigeration systems by means of data-driven strategies. To address this broad topic and with the aim to improve the efficiency of the industrial refrigeration systems, the current thesis considers three main aspects of any energy management methodology: the system performance assessment, the machinery operation improvement and the load management. Therefore, this thesis presents a novel methodology for each one of the three main aspects considered. The proposed methodologies should contemplate the necessary robustness and reliability to be applicable in real refrigeration systems. The experimental results obtained from the validation tests in the industrial refrigeration system, show the significant improvement capabilities in regard with the energy efficiency. Each one of the proposed methodologies present a promising result and can be employed individually or as a whole, composing a great basis for a data-driven based energy management framework.Avui en dia la gestió energètica ha guanyat interès degut a l'increment constant de consum per part de la indústria i els problemes de contaminació que això suposa. En aquest tema, un dels principals sectors industrials, el d'alimentació i begudes, atribueix bona part de percentatge del seu consum als sistemes de refrigeració. Aquests sistemes es veuen altament afectats per les condicions d'operació i habitualment estan formats per diverses màquines que estan continuament interactuant. Aquestes particularitats dificulten l'aplicació exitosa de metodologies d'eficiència energètica, requerint més esforços en recerca per millorar els enfocs actuals. En aquest tema, amb l'actual marc de la Indústria 4.0, la indústria està avançant cap una digitalització total de la informació dels seus processos. És en aquest context, on les capacitats prometedores de les tècniques basades en dades poden ser aplicades per a la gestió energètica. Aquesta tecnologia pot impulsar la gestió energètica cap a nous horitzons, ja que aquestes tècniques aprofiten les dades adquirides usualment en els sistemes de refrigeració per el seu propi funcionament, per a desenvolupar noves metodologies capaces d'obtenir eficiències més elevades. En conseqüència, aquesta tesi centra la seva atenció en la recerca de noves metodologies per a la gestió energètica, aplicades als sistemes de refrigeració i mitjançant estratègies basades en dades. Per abordar aquest ampli tema i amb el propòsit de millorar l'eficiència dels sistemes de refrigeració industrial, la present tesi considera els tres aspectes principals de qualsevol metodologia de gestió energètica: l'avaluació del rendiment del sistema, la millora de l'operació de la maquinària i la gestió de les càrregues. Per tant, aquesta tesi presenta una metodologia nova per a cadascun dels tres aspectes considerats. Les metodologies proposades han de contemplar la robustesa i fiabilitat necessàries per a ser aplicades en un sistema de refrigeració real. Els resultats experimentals obtinguts dels tests de validació fets en un sistema de refrigeració industrial mostren unes capacitats de millora significatives referent a l'eficiència energètica. Cadascuna de les metodologies proposades presenta un resultat prometedor i pot ser aplicada independentment o juntament amb les altres, formant una bona base per un marc de gestió energètica basat en dades

The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis