Determination of the machine energy consumption profiles in the mass-customised manufacturing

Cupek, Rafał; Ziębiński, Adam; Zonenberg, Dariusz; Drewniak, Marek

doi:10.1080/0951192x.2017.1339914

Cited by 23 publications

(30 citation statements)

References 45 publications

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“…IIoT / ICPS [372], [386], [392], [393], [366], [367], [375], [394], [395], [398] WSAN [369]- [371], [377], [396], [373], [374], [376], [378], [379], [381] NCS -Industrial Robots [384] Assembly Line [62], [364], [365], [383], [385], [387], [6], [388]- [391], [399] M2M Communication [368], [397] whole toward minimizing energy consumption is proposed in [384]. Dynamic low-power reconfiguration [364] and machine energy consumption minimization [365] are key objectives of novel assembly lines.…”

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

“…The significantly important role of data in this process is demonstrated in [383] where the collected data are shown to improve energy consumption awareness and allows the manufacturing energy management systems to make further analysis and to identify where to take actions in the manufacturing process in order to reduce the energy consumption. There have been several energy management and energy consumption optimization methods for the assembly line in the recent literature, with the most notable focusing on production control [385], forecasting models with neural networks [387], mobile service composition [388], real-time demand bidding [389], ontological modeling [390], process parameter modeling [391], machine energy consumption profiling [6], and concurrent energy data collection [399]. Methodologies and a models which reliably dimension energy scavenger properties to M2M communication requirements and network needs, allowing industries to optimize the adoption of that technologies while keeping technical risks low [368].…”

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

“…1). Novel assembly lines used in the production process are expected to boost the reconfiguration of automated manufacturing systems and provide robust operation and short production lifecycles needed by manufacturing firms so as to stay competitive in the marketplace [6]. The industrial Internet of Things (IIoT) and the industrial cyber-physical systems (ICPS) utilization in industrial settings are expected to revolutionize the way enterprises conduct their business from a holistic viewpoint, i.e., from shop-floor to business interactions, from suppliers to customers, and from design to support across the whole product and service lifecycle [7].…”

Section: Introductionmentioning

confidence: 99%

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Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…In the manufacturing sector, Cupek et al presented a k-means clustering algorithm to monitor energy efficiency of compressed air systems [19]. K-means is an iterative unsupervised clustering algorithm which is used to find groups in a dataset, with data being split into one of k groups.…”

Section: Previous Related Workmentioning

confidence: 99%

Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector

Mawson

Hughes

2020

Energy and Buildings

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The industrial and building sector demands the largest proportion of global energy, therefore adopting energy efficiency related strategies, optimization techniques and management is an important step towards global energy reduction. The use of machine learning techniques in energy forecasting is gaining popularity due to their ability to solve complex non-linear problems, however this is predominately seen in the residential and commercial sector. This study proposes and compares the use of two Deep Neural Networks, Feed Forward and Recurrent, to forecast manufacturing facility energy consumption and workshop conditions based on production schedules, climatic conditions, thermal properties of the facility building, along with building behaviour and use. The feed forward model was able to predict building energy, workshop air temperatures and humidity to an accuracy of 92.4, 99.5 and 64.8 % respectively when the model was provided with a new dataset, with the recurrent model predicting these variables to accuracies of 96.82, 99.40 and 57.60 %. The neural networks were trained with data obtained from the simulation of a medium sized manufacturing facility in the UK. Coupling simulation techniques with machine learning algorithms allows for a low cost, non-intrusive methodology of predicting and optimising building energy consumption in the manufacturing sector. Furthermore, the use of neural networks provided forecasted building energy profiles for the identification of spikes in energy consumption; an undesirable and considerable cost in the manufacturing sector, as well as the predication of manufacturing environmental conditions for condition monitoring of condition sensitive production environments. Acronyms ANNArtificial Neural Network DES Discrete Event Simulation DFNN Deep Feed Forward Neural Networks DRNN Deep Recurrent Neural Networks HMM Hidden Markov Models HVAC Heating, Ventilation and Air Conditioning MAE Mean Absolute Error MLP Multi-Layer Perceptron NAdam Nesterov-Accelerated Adaptive Moment Estimation ReLU Rectified Linear Unit Function RMSE Root Mean Square Error SVM's Support Vector Machines Nomenclature n number of samples in the training or testing set the actual output variable for entry i ̂ the predicted output variable.

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“…The study highlighted the advantage of using ANN models over conventional regression techniques. A study was found in the area of un-supervised machine learning by Cupek et al (2017) who used a k-means clustering algorithm for energy monitoring and fault detection of compressed air systems within a manufacturing environment. Using energy consumption measurements and machine behaviour observations, system state specific energy profiles were determined.…”

Section: Previous Related Workmentioning

confidence: 99%

Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments

Mawson

Hughes

2020

Optim Eng

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Manufacturing remains one of the most energy intensive sectors, additionally, the energy used within buildings for heating, ventilation and air conditioning (HVAC) is responsible for almost half of the UK’s energy demand. Commonly, these are analysed in isolation from one another. Use of machine learning is gaining popularity due to its ability to solve non-linear problems with large data sets and little knowledge about relationships between parameters. Such models use relationships between inputs and outputs to make further predictions on unseen data, without requiring any understanding regarding the system, making them highly suited to dealing with the stochastic data sets found in a manufacturing environment. This has been seen in literature for determining electrical energy demand for residential or commercial buildings, rather than manufacturing environments. This study proposes a novel method of coupling simulation with machine learning to predict indoor workshop conditions and building energy demand, in response to production schedules, outdoor conditions, building behaviour and use. Such predictions can subsequently allow for more efficient management of HVAC systems. Based upon predicted energy consumption, potential spikes were identified and manufacturing schedules subsequently optimised to reduce peak energy demand. Coupling simulation techniques with machine learning algorithms eliminates the requirement for costly and intrusive methods of data collection, providing a method of predicting and optimising building energy consumption in the manufacturing sector.

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Determination of the machine energy consumption profiles in the mass-customised manufacturing

Cited by 23 publications

References 45 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector

Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments

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