Special Issue "Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Sustainable Processes".

Deadline for manuscript submissions: closed (30 June 2021).

Special Issue Editors

Prof. Dr. Sun Hur
E-Mail Website
Guest Editor
Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Korea
Interests: data mining; industrial artificial intelligence; probabilistic OR
Prof. Dr. Jae-Yoon Jung
E-Mail Website
Guest Editor
Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Korea
Interests: industrial artificial intelligence; machine learning; data and process mining; smart manufacturing
Dr. Josue Obregon
E-Mail Website
Guest Editor
Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Korea
Interests: machine learning; smart manufacturing; smart energy; machine learning explainability; process mining; fault detection

Special Issue Information

Dear Colleagues,

We are living in the big data era in which large amounts of information are continuously created, registering all kinds of events such as the ones generated in the design, planning, control, and execution of manufacturing, logistics, and supply chain processes. Furthermore, currently, a major concern for manufacturing organizations is the environmental responsibility that has become an integral aspect of the way their production processes are designed and executed.

Sustainable production processes involve the problems of traditional production processes but with the additional goal of reducing environmental impact and minimizing waste generation. Some of the approaches of sustainable production processes are energy reduction, emissions reduction, water use reduction, and waste generation reduction. The challenges of sustainable product design, process design, energy planning, and operational principles can be enhanced and optimized by utilizing big data analysis and the recently developed related information technologies, such as the Internet of Things (IoT), cloud, and cyber–physical space (CPS) technologies.

This Special Issue on “Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process” aims to present a collection of state-of-the-art solutions to the different types of sustainable production processes using big data analysis as well as advanced analytics such as data mining, process mining, machine learning, and deep learning. Potential topics include, but are not limited to, the following:

  • (Data-driven) manufacturing processes;
  • (Data-driven) logistics and supply chain processes;
  • (Data-driven) green manufacturing;
  • (Data-driven) lean manufacturing processes;
  • (Data-driven) reconfigurable manufacturing processes;
  • (Data-driven) sustainable energy plant processes;
  • (Data-driven) factory energy management systems (FEMS);
  • Big data analytics and architectures for sustainable production processes;
  • IoT, cloud, and CPS systems for sustainable production processes;
  • Data warehouses for sustainable production processes;
  • Data mining and process mining for sustainable production processes;
  • Fault detection and system diagnostics for sustainable production processes;
  • AI, machine learning, and deep learning for sustainable production processes;
  • Theory and methods of big data analysis and advanced analytics;
  • Data-driven applications to sustainable production processes;
  • Case studies on sustainable production processes in industry.

Papers submitted to this Special Issue are expected to provide an original contribution, proposing new solutions/frameworks, improvements to existing solutions, and new applications with recent technologies. Contributions may take the form of (i) a research article, (ii) a review paper, or (iii) a case study or an industry paper.

Prof. Dr. Sun Hur
Prof. Dr. Jae-Yoon Jung
Dr. Josue Obregon
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 papers will be 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. Processes is an international peer-reviewed open access monthly 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 2000 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

  • big data analysis
  • advanced analytics
  • sustainability
  • green manufacturing
  • production process
  • data mining
  • process mining
  • machine learning
  • deep learning

Published Papers (9 papers)

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Research

Article
Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System
Processes 2021, 9(8), 1450; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9081450 - 20 Aug 2021
Viewed by 424
Abstract
Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed [...] Read more.
Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field. Full article
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Article
Residual Life Prediction for Induction Furnace by Sequential Encoder with s-Convolutional LSTM
Processes 2021, 9(7), 1121; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9071121 - 28 Jun 2021
Viewed by 456
Abstract
Induction furnaces are widely used for melting scrapped steel in small foundries and their use has recently become more frequent. The maintenance of induction furnaces is usually based on empirical decisions of the operator and an explosion can occur through operator error. To [...] Read more.
Induction furnaces are widely used for melting scrapped steel in small foundries and their use has recently become more frequent. The maintenance of induction furnaces is usually based on empirical decisions of the operator and an explosion can occur through operator error. To prevent an explosion, previous studies have utilized statistical models but have been unable to generalize the problem and have achieved a low accuracy. Herein, we propose a data-driven method for induction furnaces by proposing a novel 2D matrix called a sequential feature matrix(s-encoder) and multi-channel convolutional long short-term memory (s-ConLSTM). First, the sensor data and operation data are converted into sequential feature matrices. Then, N-sequential feature matrices are imported into the convolutional LSTM model to predict the residual life of the induction furnace wall. Based on our experimental results, our method outperforms general neural network models and enhances the safe use of induction furnaces. Full article
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Article
A Time-Series Data Generation Method to Predict Remaining Useful Life
Processes 2021, 9(7), 1115; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9071115 - 26 Jun 2021
Cited by 2 | Viewed by 870
Abstract
Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to [...] Read more.
Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model. Full article
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Article
Research on the Correlation between Work Accidents and Safety Policies in China
Processes 2021, 9(5), 805; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9050805 - 04 May 2021
Cited by 1 | Viewed by 401
Abstract
In China, safety policies interfere with the occurrence of work accidents in the form of guidance and restrictions. In this study, the impact of types of safety policies on work accident prevention is quantitatively analyzed. Based on a statistical analysis of China’s safety [...] Read more.
In China, safety policies interfere with the occurrence of work accidents in the form of guidance and restrictions. In this study, the impact of types of safety policies on work accident prevention is quantitatively analyzed. Based on a statistical analysis of China’s safety policies and work-related accidents from 2000 to 2020, the following four policy indexes that reflect the impact of safety policies are identified: the stringency level of the policy; the scope; its technical content; and its industrial target. A vector autoregressive model (VAR) is used, and a dynamic analysis of the model is conducted with an impulse response function. The model’s degree of fit is 92.9%, the number of deaths and the number of safety policies are linearly related, and the relative error between the fitted values and the real values is approximately 5%. The negative correlation between the death rate per 100 million yuan and the stringency level, scope, technical content, and industrial targets of safety policies is first weak, then strengthened, and then weakens again over time. This study finds that the importance of safety policy indicators is different; especially, the strict safety policy has a long-term negative impact on mortality. For developing countries such as China, where the safety policy system is not yet perfect, increasing the number and implementation of safety policies can significantly improve the situation of production safety. Full article
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Article
Real-World Failure Prevention Framework for Manufacturing Facilities Using Text Data
Processes 2021, 9(4), 676; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9040676 - 13 Apr 2021
Viewed by 439
Abstract
In recent years, manufacturing companies have been continuously engaging in research for the full implementation of smart factories, with many studies on methods to prevent facility failures that directly affect the productivity of the manufacturing sites. However, most studies have only analyzed sensor [...] Read more.
In recent years, manufacturing companies have been continuously engaging in research for the full implementation of smart factories, with many studies on methods to prevent facility failures that directly affect the productivity of the manufacturing sites. However, most studies have only analyzed sensor signals rather than text manually typed by operators. In addition, existing studies have not proposed an actual application system considering the manufacturing site environment but only presented a model that predicts the status or failure of the facility. Therefore, in this paper, we propose a real-world failure prevention framework that alerts the operator by providing a list of possible failure categories based on a failure pattern database before the operator starts work. The failure pattern database is constructed by analyzing and categorizing manually entered text to provide more detailed information. The performance of the proposed framework was evaluated utilizing actual manufacturing data based on scenarios that can occur in a real-world manufacturing site. The performance evaluation experiments demonstrated that the proposed framework could prevent facility failures and enhance the productivity and efficiency of the shop floor. Full article
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Article
Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management
Processes 2021, 9(2), 247; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9020247 - 29 Jan 2021
Cited by 6 | Viewed by 1188
Abstract
Adopting smart technologies for supply chain management leads to higher profits. The manufacturer and retailer are two supply chain players, where the retailer is unreliable and may not send accurate demand information to the manufacturer. As an advanced smart technology, Radio Frequency Identification [...] Read more.
Adopting smart technologies for supply chain management leads to higher profits. The manufacturer and retailer are two supply chain players, where the retailer is unreliable and may not send accurate demand information to the manufacturer. As an advanced smart technology, Radio Frequency Identification (RFID) is implemented to track and trace each product’s movement on a real-time basis in the inventory. It takes this supply chain to a smart supply chain management. This research proposes a Machine Learning (ML) approach for on-demand forecasting under smart supply chain management. Using Long-Short-Term Memory (LSTM), the demand is forecasted to obtain the exact demand information to reduce the overstock or understock situation. A measurement for the environmental effect is also incorporated with the model. A consignment policy is applied where the manufacturer controls the inventory, and the retailer gets a fixed fee along with a commission for selling each product. The manufacturer installs RFID technology at the retailer’s place. Two mathematical models are solved using a classical optimization technique. The results from those two models show that the ML-RFID model gives a higher profit than the existing traditional system. Full article
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Article
Integrating FMEA and the Kano Model to Improve the Service Quality of Logistics Centers
Processes 2021, 9(1), 51; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9010051 - 29 Dec 2020
Cited by 1 | Viewed by 897
Abstract
This study uses the logistics center of a large organic retail store in Taiwan to analyze service blueprint and workflow, identifying the potential points of failure and thus serving as a basis for quality improvement. The failure mode and effect analysis (FMEA) model [...] Read more.
This study uses the logistics center of a large organic retail store in Taiwan to analyze service blueprint and workflow, identifying the potential points of failure and thus serving as a basis for quality improvement. The failure mode and effect analysis (FMEA) model is an effective problem prevention methodology that can easily interface with many engineering and reliability methods. The utilized method integrates the failure mode and effect analysis (FMEA) and the Kano model to explore the possible occurrence of failures in the internal workflow and services of the studied logistics center. A two-stage survey was conducted. In the first stage, an investigation was conducted by 20 logistics experts on the FMEA’s key service failures. In the second stage, a questionnaire was filled out by 220 store staff to summarize the logistics service quality factors found in the Kano model. The results show that the degree of attention and satisfaction in the priority improvement items when there were service failures vary among the opinions of different internal employees and customers. The participants jointly believed that the items that need improvement are “Damaged incoming goods” and “A shortfall in the quantities of delivered goods”. Full article
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Article
Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics
Processes 2020, 8(9), 1068; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8091068 - 01 Sep 2020
Cited by 3 | Viewed by 1096
Abstract
Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, [...] Read more.
Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, a comprehensive framework that consists of three steps is proposed to predict defects and improve yield by using semi-supervised learning, time-series analysis, and classification model. In Step 1, semi-supervised learning using both labeled and unlabeled data is applied to generate quality values. In addition, feature values are predicted in time-series analysis in Step 2. Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. Compared to a conventional production plan, the suggested plan increases yield by up to 8.7%. The production plan proposed in this study is expected to contribute to not only the continuous manufacturing process but the discrete manufacturing process. In addition, it can be used in early diagnosis of equipment failure. Full article
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Article
Development of Indicator of Data Sufficiency for Feature-based Early Time Series Classification with Applications of Bearing Fault Diagnosis
Processes 2020, 8(7), 790; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8070790 - 06 Jul 2020
Cited by 2 | Viewed by 923
Abstract
Diagnosis of bearing faults is crucial in various industries. Time series classification (TSC) assigns each time series to one of a set of pre-defined classes, such as normal and fault, and has been regarded as an appropriate approach for bearing fault diagnosis. Considering [...] Read more.
Diagnosis of bearing faults is crucial in various industries. Time series classification (TSC) assigns each time series to one of a set of pre-defined classes, such as normal and fault, and has been regarded as an appropriate approach for bearing fault diagnosis. Considering late and inaccurate fault diagnosis may have a significant impact on maintenance costs, it is important to classify bearing signals as early and accurately as possible. TSC, however, has a major limitation, which is that a time series cannot be classified until the entire series is collected, implying that a fault cannot be diagnosed using TSC in advance. Therefore, it is important to classify a partially collected time series for early time series classification (ESTC), which is a TSC that considers both accuracy and earliness. Feature-based TSCs can handle this, but the problem is to determine whether a partially collected time series is enough for a decision that is still unsolved. Motivated by this, we propose an indicator of data sufficiency to determine whether a feature-based fault detection classifier can start classifying partially collected signals in order to diagnose bearing faults as early and accurately as possible. The indicator is trained based on the cosine similarity between signals that were collected fully and partially as input to the classifier. In addition, a parameter setting method for efficiently training the indicator is also proposed. The results of experiments using four benchmark datasets verified that the proposed indicator increased both accuracy and earliness compared with the previous time series classification method and general time series classification. Full article
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