Special Issue "Advanced Process Monitoring for Industry 4.0"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Supervision".

Deadline for manuscript submissions: 25 June 2021.

Special Issue Editors

Prof. Dr. Marco S. Reis
E-Mail Website
Guest Editor
CIEPQPF–Department of Chemical Engineering, University of Coimbra, 3004-531 Coimbra, Portugal
Interests: Process Analytics; Process Systems Engineering; Fault Detection, Diagnosis and Prognosis; Industrial Data Science; Chemometrics
Prof. Dr. Furong Gao
E-Mail Website
Guest Editor
Department of Chemical Engineering, The Hong Kong University of Science and Technology, Hong Kong
Interests: Batch Processes; Process Monitoring and Control; Industrial Automation; Big Data; Smart Manufacturing

Special Issue Information

Dear Colleagues,

This Special Issue aims to bring together recent advances in the broad field of Advanced Process Monitoring for Industry 4.0, including all the activities related to fault detection, diagnosis, and prognosis.

Papers on data-driven, model-based, and hybrid monitoring approaches for continuous, batch, and discrete processes are welcomed, especially when addressing emerging challenges of the new industrial technology landscape, such as, but not limited to:
• High-dimensional process monitoring;
• Dealing with heterogeneous data sources;
• Image-based process monitoring;
• Monitoring 3D objects (e.g., from additive manufacturing);
• Artificial Intelligence for process monitoring;
• Fault diagnosis and troubleshooting;
• Fault prognosis and predictive maintenance;
• Monitoring process health and equipment health;
• Integration of statistical process control and engineering process control;
• Monitoring multistage and/or distributed processes;
• Monitoring the supply chain;
• Handling stationary and nonstationary dynamics;
• Monitoring for cybersecurity;
• Applications in health.

Prof. Dr. Marco S. Reis
Prof. Dr. Furong Gao
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.

Published Papers (11 papers)

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Research

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Article
Copper Oxide Spectral Emission Detection in Chalcopyrite and Copper Concentrate Combustion
Processes 2021, 9(2), 188; https://doi.org/10.3390/pr9020188 - 20 Jan 2021
Viewed by 445
Abstract
In this research, the spectral detection of copper oxide is reported from different combustion tests of chalcopyrite particles and copper concentrates. Combustion experiments were performed in a bench reactor. In all the tests, the radiation emitted from the sulfide particle reactions was captured [...] Read more.
In this research, the spectral detection of copper oxide is reported from different combustion tests of chalcopyrite particles and copper concentrates. Combustion experiments were performed in a bench reactor. In all the tests, the radiation emitted from the sulfide particle reactions was captured in the VIS–NIR range. The obtained spectral data were processed by using the airPLS (adaptive iteratively reweighted penalized least squares) algorithm to remove their baseline, and principal component analysis (PCA) and the multivariate curve resolution method alternate least squares (MCR-ALS) methods were applied to identify the emission lines or spectral bands of copper oxides. The extracted spectral pattern is directly correlated with the emission profile reported in the literature, evidencing the potential of using spectral analysis techniques on copper sulfide combustion spectra. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
Multiscale Convolutional and Recurrent Neural Network for Quality Prediction of Continuous Casting Slabs
Processes 2021, 9(1), 33; https://doi.org/10.3390/pr9010033 - 25 Dec 2020
Viewed by 563
Abstract
Quality prediction in the continuous casting process is of great significance to the quality improvement of casting slabs. Due to the uncertainty and nonlinear relationship between the quality of continuous casting slabs (CCSs) and various factors, reliable prediction of CCS quality poses a [...] Read more.
Quality prediction in the continuous casting process is of great significance to the quality improvement of casting slabs. Due to the uncertainty and nonlinear relationship between the quality of continuous casting slabs (CCSs) and various factors, reliable prediction of CCS quality poses a challenge to the steel industry. However, traditional prediction models based on domain knowledge and expertise are difficult to adapt to the changes in multiple operating conditions and raw materials from various enterprises. To meet the challenge, we propose a framework with a multiscale convolutional and recurrent neural network (MCRNN) for reliable CCS quality prediction. The proposed framework outperforms conventional time series classification methods with better feature representation since the input is transformed at different scales and frequencies, which captures both long-term trends and short-term changes in time series. Moreover, we generate different category distributions based on the random undersampling (RUS) method to mitigate the impact of the skewed data distribution due to the natural imbalance of continuous casting data. The experimental results and comprehensive comparison with the state-of-the-art methods show the superiority of the proposed MCRNN framework, which has not only satisfactory prediction performance but also good potential to improve continuous casting process understanding and CCS quality. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
First Principles Statistical Process Monitoring of High-Dimensional Industrial Microelectronics Assembly Processes
Processes 2020, 8(11), 1520; https://doi.org/10.3390/pr8111520 - 23 Nov 2020
Cited by 1 | Viewed by 688
Abstract
Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to [...] Read more.
Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to evaluate the stability of the process and, after confirming that it is stable, to calibrate a monitoring procedure, i.e., estimate the reference model and set the control limits for the monitoring statistics. The basic assumption is that all relevant “common causes” of variation appear well represented in this reference dataset (using the terminology adopted by the founding father of process monitoring, Walter A. Shewhart). Otherwise, false alarms will inevitably occur during the implementation of the monitoring scheme. However, we argue and demonstrate in this article, that this assumption is often not met in modern industrial systems. Therefore, we introduce a new approach based on the rigorous mechanistic modeling of the dominant modes of common cause variation and the use of stochastic computational simulations to enrich the historical dataset with augmented data representing a comprehensive coverage of the actual operational space. We show how to compute the monitoring statistics and set their control limits, as well as to conduct fault diagnosis when an abnormal event is declared. The proposed method, called AGV (Artificial Generation of common cause Variability) is applied to a Surface Mount Technology (SMT) production line of Bosch Car Multimedia, where more than 17 thousand product variables are simultaneously monitored. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
Multivariate Six Sigma: A Case Study in Industry 4.0
Processes 2020, 8(9), 1119; https://doi.org/10.3390/pr8091119 - 09 Sep 2020
Cited by 1 | Viewed by 1103
Abstract
The complex data characteristics collected in Industry 4.0 cannot be efficiently handled by classical Six Sigma statistical toolkit based mainly in least squares techniques. This may refrain people from using Six Sigma in these contexts. The incorporation of latent variables-based multivariate statistical techniques [...] Read more.
The complex data characteristics collected in Industry 4.0 cannot be efficiently handled by classical Six Sigma statistical toolkit based mainly in least squares techniques. This may refrain people from using Six Sigma in these contexts. The incorporation of latent variables-based multivariate statistical techniques such as principal component analysis and partial least squares into the Six Sigma statistical toolkit can help to overcome this problem yielding the Multivariate Six Sigma: a powerful process improvement methodology for Industry 4.0. A multivariate Six Sigma case study based on the batch production of one of the star products at a chemical plant is presented. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
Modeling of Spiral Wound Membranes for Gas Separations—Part II: Data Reconciliation for Online Monitoring
Processes 2020, 8(9), 1035; https://doi.org/10.3390/pr8091035 - 25 Aug 2020
Cited by 2 | Viewed by 770
Abstract
The present work presents a methodology based on data reconciliation to monitor membrane separation processes reliably, online and in real time for the first time. The proposed methodology was implemented in accordance with the following steps: data acquisition; data pre-treatment; data characterization; data [...] Read more.
The present work presents a methodology based on data reconciliation to monitor membrane separation processes reliably, online and in real time for the first time. The proposed methodology was implemented in accordance with the following steps: data acquisition; data pre-treatment; data characterization; data reconciliation; gross error detection; and critical evaluation of measured data with a soft sensor. The acquisition of data constituted the slowest stage of the monitoring process, as expected in real-time applications. The pre-treatment stage was fundamental to assure the robustness of the code and the initial characterization of collected data was carried out offline. The characterization of the data showed that steady-state modeling of the process would be appropriate, also allowing the implementation of faster numerical procedures for the data reconciliation step. The data reconciliation step performed well, quickly and consistently. Thus, data reconciliation allowed the estimation of unmeasured variables, playing the role of a soft sensor and allowing the future installation of a digital twin. Additionally, monitoring of measurement bias constituted a tool for measurement diagnosis. As shown in the manuscript, the proposed methodology can be successfully implemented online and in real time for monitoring of membrane separation processes, as shown through a real dashboard web application developed for monitoring of an actual industrial site. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network
Processes 2020, 8(6), 704; https://doi.org/10.3390/pr8060704 - 18 Jun 2020
Cited by 16 | Viewed by 681
Abstract
Non-intrusive load monitoring (NILM) is an effective way to achieve demand-side measurement and energy efficiency optimization. This paper studies a method of non-intrusive on-line load monitoring under a high-frequency mode of electric data acquisition, which enables the NILM to be automated and in [...] Read more.
Non-intrusive load monitoring (NILM) is an effective way to achieve demand-side measurement and energy efficiency optimization. This paper studies a method of non-intrusive on-line load monitoring under a high-frequency mode of electric data acquisition, which enables the NILM to be automated and in real-time, including the short-term construction of a dynamic signature library and continuous on-line load identification. Firstly, in the short initial operation phase, load separation and category determination are carried out to construct the load waveform library of the monitoring user. Then, the continuous load monitoring phase begins. Based on the data of each user’s signature library, the decomposition waveforms are classified by convolutional neural network models that are constructed to be suitable for each signature library in order to realize load identification. The real-time power consumption status of the load can be obtained continuously. In this paper, the electricity data of actual users are collected and used to perform the experiments, which show that the proposed method can construct the load signature library adaptively for different users. Meanwhile, the classification of the convolutional neural network model based on a library constructed in actual operation ensures the real-time and accuracy of load monitoring. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production
Processes 2020, 8(6), 634; https://doi.org/10.3390/pr8060634 - 26 May 2020
Cited by 1 | Viewed by 1578
Abstract
Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production [...] Read more.
Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
AOC-OPTICS: Automatic Online Classification for Condition Monitoring of Rolling Bearing
Processes 2020, 8(5), 606; https://doi.org/10.3390/pr8050606 - 20 May 2020
Cited by 1 | Viewed by 976
Abstract
Bearings are essential components in rotating machines. They ensure the rotation and power transmission. So, these components are essential elements for industrial machines. Thus, real-time monitoring is required to detect a possible anomaly, diagnose the failure of rolling bearing and follow its evolution. [...] Read more.
Bearings are essential components in rotating machines. They ensure the rotation and power transmission. So, these components are essential elements for industrial machines. Thus, real-time monitoring is required to detect a possible anomaly, diagnose the failure of rolling bearing and follow its evolution. This paper presents a methodology for automatic online implementation of fault diagnosis of rolling bearings, by AOC-OPTICS (automatic online classification monitoring based on ordering points to identify clustering structure, OPTICS). The algorithm consists of three phases namely: initialization, detection and follow-up. These phases use the combination of features extraction methods, smart ranking, features weighting and classification by the OPTICS method. Two methods have been integrated in the dimension reduction step to improve the efficiency of detection and the followed of the defect (relief method and t-distributed stochastic neighbor embedding method). Thus, the determination of the internal parameters of the OPTICS method is improved. A regression model and exponential model are used to track the fault. The analytical simulations discuss the influence of parameters automation. Experimental validation shows detection with 100% accuracy and regression models of monitoring reaching R 2 = 0.992 . Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry
Processes 2020, 8(2), 224; https://doi.org/10.3390/pr8020224 - 14 Feb 2020
Cited by 1 | Viewed by 1097
Abstract
In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and effects [...] Read more.
In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and effects analysis is a technique that is used in industry to identify possible failures that may occur and the effects of these failures on the system. Meanwhile, risk priority number is a numeric value that is calculated by multiplying three associated parameters namely severity, occurrence and detectability. The value of risk priority number determines the next actions to be made. A dataset that includes a one-year registry of 1532 failures with their description, severity, occurrence, and detectability is used to develop four models to predict the values of severity, occurrence, and detectability. Meanwhile, the resulted models are evaluated using 10% of the dataset. Evaluation results show that the proposed models have high accuracy whereas the average value of precision, recall, and F1 score are in the range of 86.6–93.2%, 67.9–87.9%, 0.892–0.765% respectively. The proposed work helps in carrying out failure mode and effects analysis in a more efficient way as compared to the conventional techniques. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Article
Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks
Processes 2020, 8(1), 73; https://doi.org/10.3390/pr8010073 - 06 Jan 2020
Viewed by 1118
Abstract
Identifying abnormal process operation with spatial-temporal data remains an important and challenging work in many practical situations. Although spatial-temporal data identification has been extensively studied in some domains, such as public health, geological condition, and environment pollution, the challenge associated with designing accurate [...] Read more.
Identifying abnormal process operation with spatial-temporal data remains an important and challenging work in many practical situations. Although spatial-temporal data identification has been extensively studied in some domains, such as public health, geological condition, and environment pollution, the challenge associated with designing accurate and convenient recognition schemes is very rarely addressed in modern manufacturing processes. This paper proposes a general recognition framework for identifying abnormal process with spatial-temporal data by employing a convolutional neural network (CNN) model. Firstly, motivated by the pasting case study, the spatial-temporal data are transformed into process images for capturing spatial and temporal interrelationship. Then, the CNN recognition model is presented for identifying different types of these process images, leading to the identification of abnormal process with spatial-temporal data. The specific architecture parameters of CNN are determined step by step. According to the performance comparison with alternative methods, the proposed method is able to accurately identify the abnormal process with spatial-temporal data. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Review

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Review
A Review of Data Mining Applications in Semiconductor Manufacturing
Processes 2021, 9(2), 305; https://doi.org/10.3390/pr9020305 - 06 Feb 2021
Cited by 2 | Viewed by 1028
Abstract
For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. [...] Read more.
For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn. Full article
(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1.  Title: Multi-scale Convolutional and Recurrent Neural Network for Quality Prediction of Cold Rolling Process

Abstract: The quality prediction in cold rolling process is of great significance to effective process control and total quality improvement. Flatness is a key geometrical feature of strip products in a cold rolling process. Due to the uncertainty and non-linear relationship between the flatness of strip products and various factors, prediction of flatness quality is a challenge. However, traditional prediction models based on domain knowledge and expertise are difficult to adapt to the changes in multiple operating conditions and raw materials from various enterprises. To meet the challenge, we propose a flatness pattern prediction framework composed of a multiscale convolutional and recurrent neural network (MCRNN) to handle the varying operating condition during the rolling of products with different specifications and to realize an effective flatness feedback control strategy. The proposed framework MCRNN consists of three parts: data acquisition, quality prediction, and dynamic control. In addition, in the case of the natural imbalance of continuous casting data, we generate different class distributions based on random under-sampling (RUS) method to mitigate the impact of the skewed data distribution. The experimental results and the comprehensive comparison with the state-of-the-art methods show the superiority of the proposed MCRNN framework, which has not only the satisfactory prediction performance, but good potentials to improve process understanding and strip flatness quality.

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