Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advanced Manufacturing Informatics, Energy and Sustainability

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 18429

Share This Special Issue

Special Issue Editors

Prof. Dr. Seán McLoone

E-Mail Website
Guest Editor

School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, 16A Malone Road, Belfast BT9 5BN, UK
Interests: manufacturing informatics; soft sensing, predictive maintenance; process monitoring; blind sensor characterisation; computational intelligence techniques
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gian Antonio Susto

E-Mail Website
Guest Editor

Department of Information Engineering, University of Padova, 35131 Padova, Italy
Interests: soft sensors; data mining; machine learning; deep learning; virtual metrology

Special Issue Information

Dear Colleagues

We are inviting submissions for a Special Issue of Energies on "Advanced Manufacturing Informatics, Energy and Sustainability". Manufacturing is undergoing a transformative change at present—the 4^th Industrial revolution (Industry 4.0)—enabled by the rapid developments in Internet-of-Things technologies and major advances in areas such as autonomous robotics, artificial intelligence, cloud computing, and additive manufacturing. These technologies have the potential to deliver step changes in the productivity, flexibility, resilience, responsiveness, and energy efficiency of manufacturing systems and associated supply chains and ultimately drive down costs, improve product quality, and minimise waste (materials and energy). Manufacturing informatics is at the heart of this revolution. The pervasive availability of data from within the factory as well as from the production supply chain and from products in service, afforded by the adoption of Internet-of-Things technologies, creates the potential for data analytics and artificial intelligence/machine learning techniques to be employed to optimize the performance of manufacturing systems with respect to performance metrics such as product quality, throughput, energy usage, and cost.

The objective of this Special Issue is to present contributions from practitioners and researchers on the state of the art of manufacturing informatics—from theory to applications. Topics of interest for publication include, but are not limited to:

Soft sensing for enhanced process monitoring and real-time control
Machine learning for adaptive control and optimization in manufacturing
Traditional and cloud-based smart monitoring systems for fault detection and classification, energy usage, anomaly detection, machine health assessment, and predictive maintenance
Design of smart products with embedded machine learning capabilities to enhance energy efficiency and sustainability
Informatics enabled carbon footprint reduction
Data-driven approaches to reconfigurable manufacturing systems
Self-optimizing, self-configuring, and self-diagnosing systems and equipment
Production scheduling optimisation for green manufacturing
Informatics-based methods and technologies for manufacturing system lifetime extension and resource waste reduction
Case studies on informatics-based refurbishment, revamping, and upgrading of industrial equipment and plants

Prof. Dr. Seán McLoone
Prof. Dr. Gian Antonio Susto
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

manufacturing Informatics
soft sensing
predictive maintenance
fault detection
smart manufacturing
intelligent automation
machine learning
artificial intelligence
big data

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

24 pages, 3849 KiB

Open AccessArticle

Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection

by Tommaso Barbariol, Enrico Feltresi and Gian Antonio Susto

Energies 2020, 13(12), 3136; https://doi.org/10.3390/en13123136 - 17 Jun 2020

Cited by 23 | Viewed by 3667

Abstract

Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams. The approach is validated both on real and synthetic data. Full article

(This article belongs to the Special Issue Advanced Manufacturing Informatics, Energy and Sustainability)

► Show Figures

Figure 1

13 pages, 1725 KiB

Open AccessArticle

Laundry Fabric Classification in Vertical Axis Washing Machines Using Data-Driven Soft Sensors

by Marco Maggipinto, Elena Pesavento, Fabio Altinier, Giuliano Zambonin, Alessandro Beghi and Gian Antonio Susto

Energies 2019, 12(21), 4080; https://doi.org/10.3390/en12214080 - 25 Oct 2019

Cited by 7 | Viewed by 3575

Abstract

Embedding household appliances with smart capabilities is becoming common practice among major fabric-care producers that seek competitiveness on the market by providing more efficient and easy-to-use products. In Vertical Axis Washing Machines (VA-WM), knowing the laundry composition is fundamental to setting the washing cycle properly with positive impact both on energy/water consumption and on washing performance. An indication of the load typology composition (cotton, silk, etc.) is typically provided by the user through a physical selector that, unfortunately, is often placed by the user on the most general setting due to the discomfort of manually changing configurations. An automated mechanism to determine such key information would thus provide increased user experience, better washing performance, and reduced consumption; for this reason, we present here a data-driven soft sensor that exploits physical measurements already available on board a commercial VA-WM to provide an estimate of the load typology through a machine-learning-based statistical model of the process. The proposed method is able to work in a resource-constrained environment such as the firmware of a VA-WM. Full article

(This article belongs to the Special Issue Advanced Manufacturing Informatics, Energy and Sustainability)

► Show Figures

Graphical abstract

24 pages, 1597 KiB

Open AccessArticle

Machine Learning-Based Soft Sensors for the Estimation of Laundry Moisture Content in Household Dryer Appliances

by Giuliano Zambonin, Fabio Altinier, Alessandro Beghi, Leandro dos Santos Coelho, Nicola Fiorella, Terenzio Girotto, Mirco Rampazzo, Gilberto Reynoso-Meza and Gian Antonio Susto

Energies 2019, 12(20), 3843; https://doi.org/10.3390/en12203843 - 11 Oct 2019

Cited by 7 | Viewed by 3316

Abstract

The aim is to develop soft sensors (SSs) to provide an estimation of the laundry moisture of clothes introduced in a household Heat Pump Washer–Dryer (WD-HP) appliance. The developed SS represents a cost-effective alternative to physical sensors, and it aims at improving the WD-HP performance in terms of drying process efficiency of the automatic drying cycle. To this end, we make use of appropriate Machine Learning models, which are derived by means of Regularization and Symbolic Regression methods. These methods connect easy-to-measure variables with the laundry moisture content, which is a difficult and costly to measure variable. Thanks to the use of SSs, the laundry moisture estimation during the drying process is effectively available. The proposed models have been tested by exploiting real data through an experimental test campaign on household drying machines. Full article

(This article belongs to the Special Issue Advanced Manufacturing Informatics, Energy and Sustainability)

► Show Figures

Graphical abstract

11 pages, 245 KiB

Open AccessFeature PaperArticle

Effect of Irrelevant Variables on Faulty Wafer Detection in Semiconductor Manufacturing

by Dongil Kim and Seokho Kang

Energies 2019, 12(13), 2530; https://doi.org/10.3390/en12132530 - 1 Jul 2019

Cited by 8 | Viewed by 2516

Abstract

Machine learning has been applied successfully for faulty wafer detection tasks in semiconductor manufacturing. For the tasks, prediction models are built with prior data to predict the quality of future wafers as a function of their precedent process parameters and measurements. In real-world problems, it is common for the data to have a portion of input variables that are irrelevant to the prediction of an output variable. The inclusion of many irrelevant variables negatively affects the performance of prediction models. Typically, prediction models learned by different learning algorithms exhibit different sensitivities with regard to irrelevant variables. Algorithms with low sensitivities are preferred as a first trial for building prediction models, whereas a variable selection procedure is necessarily considered for highly sensitive algorithms. In this study, we investigate the effect of irrelevant variables on three well-known representative learning algorithms that can be applied to both classification and regression tasks: artificial neural network, decision tree (DT), and k-nearest neighbors (k-NN). We analyze the characteristics of these learning algorithms in the presence of irrelevant variables with different model complexity settings. An empirical analysis is performed using real-world datasets collected from a semiconductor manufacturer to examine how the number of irrelevant variables affects the behavior of prediction models trained with different learning algorithms and model complexity settings. The results indicate that the prediction accuracy of k-NN is highly degraded, whereas DT demonstrates the highest robustness in the presence of many irrelevant variables. In addition, a higher model complexity of learning algorithms leads to a higher sensitivity to irrelevant variables. Full article

(This article belongs to the Special Issue Advanced Manufacturing Informatics, Energy and Sustainability)

► Show Figures

Figure 1

18 pages, 3019 KiB

Open AccessFeature PaperArticle

Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings

by Chia-Yen Lee, Ting-Syun Huang, Meng-Kun Liu and Chen-Yang Lan

Energies 2019, 12(5), 801; https://doi.org/10.3390/en12050801 - 28 Feb 2019

Cited by 25 | Viewed by 4599

Abstract

Electric motors are widely used in our society in applications like cars, household appliances, industrial equipment, etc. Costly failures can be avoided by establishing predictive maintenance (PdM) policies or mechanisms for the repair or replacement of the components in electric motors. One of key components in the motors are bearings, and it is critical to measure the key features of bearings to support maintenance decision. This paper proposes a data science approach with embedded statistical data mining and a machine learning algorithm to predict the remaining useful life (RUL) of the bearings in a motor. The vibration signals of the bearings are collected from the experimental platform, and fault detection devices are developed to extract the important features of bearings in time domain and frequency domain. Regression-based models are developed to predict the RUL, and weighted least squares regression (WLS) and feasible generalized least squares regression (FGLS) are used to address the heteroscedasticity problem in the vibration dataset. Support vector regression (SVR) is also applied for prediction benchmarking. Case studies show that the proposed data science approach handled large datasets with ease and predicted the RUL of the bearings with accuracy. The features extracted from time domain are more significant than those extracted from frequency domain, and they benefit engineering knowledge. According to the RUL results, the PdM policy is developed for component replacement at the right moment to avoid the catastrophic equipment failure. Full article

(This article belongs to the Special Issue Advanced Manufacturing Informatics, Energy and Sustainability)

► Show Figures

Journal Menu

Journal Browser

Advanced Manufacturing Informatics, Energy and Sustainability

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI