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Mathematics
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Advanced Statistical Control and Predictive Maintenance Models for Industry 4.0
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Advanced Statistical Control and Predictive Maintenance Models for Industry 4.0

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 4040

Special Issue Editors

Dr. Hai-Canh VU

E-Mail Website
Guest Editor
Roberval Loboratory, Compiègne University of Technology, 60200 Compiègne, France
Interests: predictive maintenance; prognostics and health management; machine learning; Industry 4.0
Dr. Nassim Boudaoud

E-Mail Website
Guest Editor
Roberval Loboratory, Compiègne University of Technology, 60200 Compiègne, France
Interests: statistical process control; prognostics and health management; machine learning; Industry 4.0

Special Issue Information

Dear Colleagues,

The production processes of the future (Industry 4.0) are intelligent systems composed of several cyber-physical modules (CPMs). A CPM can be an integration of a machine (physical part) with its intelligent sensors (IoT: Internet of Things) and actuators to enable it to automatically collect information about the machine’s condition and also the product quality. This information is very useful for maintenance planning and quality control of these production processes. Correct implementation of both maintenance and quality control can help to avoid machine failures and improve processes’ performances. However, the collected data about a production process are complex and usually recognized as big data. The exploitation of these data is then very challenging. For this reason, this Special Issue collects papers with the aim to develop advanced models based on machine learning/deep learning for predictive maintenance planning and statistical control of the future production processes. We expect that these advanced models will be a theoretical basis for improving the performance of the industrial processes and support the zero-failure and zero-defect concepts of Industry 4.0.

Dr. Hai-Canh VU
Dr. Nassim Boudaoud
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. Mathematics 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.

Published Papers (2 papers)

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Research

25 pages, 1736 KiB  
Article
Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines
by Khoa Tran, Hai-Canh Vu, Lam Pham, Nassim Boudaoud and Ho-Si-Hung Nguyen
Mathematics 2024, 12(10), 1569; https://doi.org/10.3390/math12101569 - 17 May 2024
Viewed by 565
Abstract
Predictive maintenance (PdM) is one of the most powerful maintenance techniques based on the estimation of the remaining useful life (RUL) of machines. Accurately estimating the RUL is crucial to ensure the effectiveness of PdM. However, current methods have limitations in fully exploring [...] Read more.
Predictive maintenance (PdM) is one of the most powerful maintenance techniques based on the estimation of the remaining useful life (RUL) of machines. Accurately estimating the RUL is crucial to ensure the effectiveness of PdM. However, current methods have limitations in fully exploring condition monitoring data, particularly vibration signals, for RUL estimation. To address these challenges, this research presents a novel Robust Multi-Branch Deep Learning (Robust-MBDL) model. Robust-MBDL stands out by leveraging diverse data sources, including raw vibration signals, time–frequency representations, and multiple feature domains. To achieve this, it adopts a specialized three-branch architecture inspired by efficient network designs. The model seamlessly integrates information from these branches using an advanced attention-based Bi-LSTM network. Furthermore, recognizing the importance of data quality, Robust-MBDL incorporates an unsupervised LSTM-Autoencoder for noise reduction in raw vibration data. This comprehensive approach not only overcomes the limitations of existing methods but also leads to superior performance. Experimental evaluations on benchmark datasets such as XJTU-SY and PRONOSTIA showcase Robust-MBDL’s efficacy, particularly in rotating machine health prognostics. These results underscore its potential for real-world applications, heralding a new era in predictive maintenance practices. Full article
(This article belongs to the Special Issue Advanced Statistical Control and Predictive Maintenance Models for Industry 4.0)
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21 pages, 5864 KiB  
Article
Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel
by Tayfun Uyanık, Yunus Yalman, Özcan Kalenderli, Yasin Arslanoğlu, Yacine Terriche, Chun-Lien Su and Josep M. Guerrero
Mathematics 2022, 10(22), 4167; https://doi.org/10.3390/math10224167 - 8 Nov 2022
Cited by 5 | Viewed by 2962
Abstract
In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of [...] Read more.
In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of guidelines that increase energy efficiency and reduce shipborne emissions. The main engine shaft power (MESP) and fuel consumption (FC) are the critical components used in ship energy efficiency calculations. Errors in ship energy efficiency calculation methodologies are also caused by misinterpretation of these values. This study aims to predict the MESP and FC of a container ship with the help of data-driven methodologies utilizing actual voyage data to assist in the calculation process of the ship’s energy efficiency indexes appropriately. The algorithms’ prediction success was measured using the RMSE, MAE, and R2 error metrics. When the simulation results were analyzed, the Deep Neural Network and Bayes algorithms predicted MESP best with 0.000001 and 0.000002 RMSE, 0.000987 and 0.000991 MAE, and 0.999999 R2, respectively, while the Multiple-Linear Regression and Kernel Ridge algorithms estimated FC best with 0.000208 and 0.000216 RMSE, 0.001375 and 0.001471 MAE, and 0.999999 R2, respectively. Full article
(This article belongs to the Special Issue Advanced Statistical Control and Predictive Maintenance Models for Industry 4.0)
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