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Advanced Process Monitoring for Industry 4.0

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

Deadline for manuscript submissions: closed (25 June 2021) | Viewed by 57530

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Special Issue Editors

Dr. Marco S. Reis

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Guest Editor

Department of Chemical Engineering, University of Coimbra, Polo II, Rua Sílvio Lima, 3030-790 Coimbra, Portugal
Interests: process analytics; process systems engineering; fault detection, diagnosis and prognosis; industrial data science; chemometrics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Furong Gao

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Guest Editor

Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
Interests: battery modeling; lithium compounds; secondary cells; process modeling; control and monitoring; polymer processing

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 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. 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 2400 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 (13 papers)

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Editorial

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3 pages, 177 KiB

Open AccessEditorial

Special Issue “Advanced Process Monitoring for Industry 4.0”

by Marco S. Reis and Furong Gao

Processes 2021, 9(8), 1432; https://doi.org/10.3390/pr9081432 - 19 Aug 2021

Cited by 1 | Viewed by 1460

Abstract

Industry 4 [...] Full article

(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)

Research

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21 pages, 4004 KiB

Open AccessArticle

Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes

by Luping Zhao, Xin Huang and Hao Yu

Processes 2021, 9(8), 1321; https://doi.org/10.3390/pr9081321 - 29 Jul 2021

Cited by 4 | Viewed by 1450

Abstract

In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes. Full article

(This article belongs to the Special Issue Advanced Process Monitoring for Industry 4.0)

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13 pages, 6771 KiB

Open AccessArticle

Copper Oxide Spectral Emission Detection in Chalcopyrite and Copper Concentrate Combustion

by Gonzalo Reyes, Walter Diaz, Carlos Toro, Eduardo Balladares, Sergio Torres, Roberto Parra and Alejandro Vásquez

Processes 2021, 9(2), 188; https://doi.org/10.3390/pr9020188 - 20 Jan 2021

Cited by 6 | Viewed by 2553

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 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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16 pages, 1064 KiB

Open AccessArticle

Multiscale Convolutional and Recurrent Neural Network for Quality Prediction of Continuous Casting Slabs

by Xing Wu, Hanlu Jin, Xueming Ye, Jianjia Wang, Zuosheng Lei, Ying Liu, Jie Wang and Yike Guo

Processes 2021, 9(1), 33; https://doi.org/10.3390/pr9010033 - 25 Dec 2020

Cited by 11 | Viewed by 2420

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 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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27 pages, 16998 KiB

Open AccessArticle

First Principles Statistical Process Monitoring of High-Dimensional Industrial Microelectronics Assembly Processes

by Tiago J. Rato, Pedro Delgado, Cristina Martins and Marco S. Reis

Processes 2020, 8(11), 1520; https://doi.org/10.3390/pr8111520 - 23 Nov 2020

Cited by 10 | Viewed by 2488

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 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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20 pages, 5453 KiB

Open AccessFeature PaperArticle

Multivariate Six Sigma: A Case Study in Industry 4.0

by Daniel Palací-López, Joan Borràs-Ferrís, Larissa Thaise da Silva de Oliveria and Alberto Ferrer

Processes 2020, 8(9), 1119; https://doi.org/10.3390/pr8091119 - 09 Sep 2020

Cited by 20 | Viewed by 4199

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 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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31 pages, 5070 KiB

Open AccessArticle

Modeling of Spiral Wound Membranes for Gas Separations—Part II: Data Reconciliation for Online Monitoring

by Diego Queiroz Faria de Menezes, Marília Caroline Cavalcante de Sá, Tahyná Barbalho Fontoura, Thiago Koichi Anzai, Fabio Cesar Diehl, Pedro Henrique Thompson and Jose Carlos Pinto

Processes 2020, 8(9), 1035; https://doi.org/10.3390/pr8091035 - 25 Aug 2020

Cited by 10 | Viewed by 2917

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 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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20 pages, 5952 KiB

Open AccessArticle

Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network

by Xin Wu, Dian Jiao and Yu Du

Processes 2020, 8(6), 704; https://doi.org/10.3390/pr8060704 - 18 Jun 2020

Cited by 23 | Viewed by 2314

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 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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29 pages, 10570 KiB

Open AccessFeature PaperArticle

Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production

by Javaneh Ramezani and Javad Jassbi

Processes 2020, 8(6), 634; https://doi.org/10.3390/pr8060634 - 26 May 2020

Cited by 28 | Viewed by 5461

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 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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17 pages, 6091 KiB

Open AccessArticle

AOC-OPTICS: Automatic Online Classification for Condition Monitoring of Rolling Bearing

by Hassane Hotait, Xavier Chiementin and Lanto Rasolofondraibe

Processes 2020, 8(5), 606; https://doi.org/10.3390/pr8050606 - 20 May 2020

Cited by 6 | Viewed by 2644

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. 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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16 pages, 2456 KiB

Open AccessArticle

Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry

by Sami Sader, István Husti and Miklós Daróczi

Processes 2020, 8(2), 224; https://doi.org/10.3390/pr8020224 - 14 Feb 2020

Cited by 12 | Viewed by 7399

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 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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19 pages, 1509 KiB

Open AccessArticle

Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks

by Yumin Liu, Zheyun Zhao, Shuai Zhang and Uk Jung

Processes 2020, 8(1), 73; https://doi.org/10.3390/pr8010073 - 06 Jan 2020

Cited by 4 | Viewed by 2585

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 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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38 pages, 4475 KiB

Open AccessFeature PaperReview

A Review of Data Mining Applications in Semiconductor Manufacturing

by Pedro Espadinha-Cruz, Radu Godina and Eduardo M. G. Rodrigues

Processes 2021, 9(2), 305; https://doi.org/10.3390/pr9020305 - 06 Feb 2021

Cited by 50 | Viewed by 18233

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. 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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