Special Issue "Advanced Process Monitoring for Industry 4.0"

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

Deadline for manuscript submissions: 30 September 2020.

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 1400 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2020 an APC of 1500 CHF applies. 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 (2 papers)

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Research

Open AccessArticle
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 (registering DOI) - 14 Feb 2020
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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Open AccessArticle
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
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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