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Applied Sciences
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Advanced Digital Technologies for the Integration of Production and Maintenance
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Advanced Digital Technologies for the Integration of Production and Maintenance

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Additive Manufacturing Technologies".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 32054

Special Issue Editors

Dr. Simone Arena

E-Mail Website
Guest Editor
Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy
Interests: asset management; maintenance; manufacturing systems; machine learning; fault diagnosis; health prognosis; condition monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luca Fumagalli

E-Mail Website
Guest Editor
Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini 4/b, 20156 Milano, Italy
Interests: ICT for manufacturing; smart manufacturing; Industry 4.0; production management; maintenance management; operations management; condition monitoring
Dr. Mirco Peron

E-Mail Website
Guest Editor
Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, 76130 Mont-Saint-Aignan, France
Interests: supply chain management; digital supply chain management; circular economy; hydrogen supply chain; decision support systems; additive manufacturing; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Production scheduling and maintenance management are responsibilities of different functions often corresponding to different departments of a manufacturing company. The relationship between these two functions is conflicting in nature, since maintenance tasks take time that could be differently used for production while, in the other hand, delaying these activities to promote production may increase the probability of machines failures.

A joint vision would allow to achieve an optimized management, in coherence with the single objectives of the two departments, but also aligned with the overall goal of the company: enhanced results are in fact expected, by means of a joint planning and control approach, both in terms of cost savings and improved technical performances. In this scenario, the widespread use of digital technologies driving Industry 4.0 paradigm, as IoT-enabled tools, can help improve collaboration between the two activities and decision-making processes in pursuit of common organizational goals. The special session considers contributions providing models, new methodologies, techniques, and frameworks for the integration and the optimization of production and maintenance operations. Within this scope, the special issue focuses on contributions that leverage on new technologies in the scope of Industry 4.0 and that can foster sustainable goals.

Dr. Simone Arena
Prof. Dr. Luca Fumagalli
Dr. Mirco Peron
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. Applied Sciences 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 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.

Keywords

  • maintenance
  • sustainability
  • scheduling
  • artificial intelligence
  • production planning
  • Industry 4.0
  • information system
  • decision support system (DSS)

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Published Papers (7 papers)

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Editorial

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2 pages, 179 KiB  
Editorial
Special Issue “Advanced Digital Technologies for the Integration of Production and Maintenance”
by Simone Arena, Luca Fumagalli and Mirco Peron
Appl. Sci. 2022, 12(23), 12417; https://doi.org/10.3390/app122312417 - 4 Dec 2022
Viewed by 1145
Abstract
Production scheduling and maintenance management are responsibilities of different functions often corresponding to different departments of a manufacturing company [...] Full article
(This article belongs to the Special Issue Advanced Digital Technologies for the Integration of Production and Maintenance)

Research

Jump to: Editorial

29 pages, 759 KiB  
Article
Data Science Application for Failure Data Management and Failure Prediction in the Oil and Gas Industry: A Case Study
by Simone Arena, Giuseppe Manca, Stefano Murru, Pier Francesco Orrù, Roberta Perna and Diego Reforgiato Recupero
Appl. Sci. 2022, 12(20), 10617; https://doi.org/10.3390/app122010617 - 20 Oct 2022
Cited by 9 | Viewed by 3007
Abstract
In the industrial domain, maintenance is essential to guarantee the correct operations, availability, and efficiency of machinery and systems. With the advent of Industry 4.0, solutions based on machine learning can be used for the prediction of future failures by exploiting historical failure [...] Read more.
In the industrial domain, maintenance is essential to guarantee the correct operations, availability, and efficiency of machinery and systems. With the advent of Industry 4.0, solutions based on machine learning can be used for the prediction of future failures by exploiting historical failure data. Most of the time, these historical data have been collected by companies without a specific structure, schema, or even best practices, resulting in a potential loss of knowledge. In this paper, we analyze the historical data on maintenance alerts of the components of a revamping topping plant (referred to as RT2) belonging to the SARAS group. This analysis is done in collaboration with the ITALTELECO company, a partner of SARAS, that provided the necessary data. The pre-processing methodology to clean and fill these data and extract features useful for a prediction task will be shown. More in detail, we show the process to fill missing fields of these data to provide (i) a category for each fault by using simple natural language processing techniques and performing a clustering, and (ii) a data structure that can enable machine learning models and statistical approaches to perform reliable failure predictions. The data domain in which this methodology is applied is oil and gas, but it may be generalized and reformulated in various industrial and/or academic fields. The ultimate goal of our work is to obtain a procedure that is simple and can be applied to provide strategic support for the definition of an adequate maintenance plan. Full article
(This article belongs to the Special Issue Advanced Digital Technologies for the Integration of Production and Maintenance)
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17 pages, 3479 KiB  
Article
Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study
by Roberto Sala, Fabiana Pirola, Giuditta Pezzotta and Sergio Cavalieri
Appl. Sci. 2022, 12(15), 7395; https://doi.org/10.3390/app12157395 - 22 Jul 2022
Cited by 7 | Viewed by 3453
Abstract
Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service [...] Read more.
Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it. Full article
(This article belongs to the Special Issue Advanced Digital Technologies for the Integration of Production and Maintenance)
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17 pages, 9217 KiB  
Article
An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies
by Agne Paulauskaite-Taraseviciene, Eimantas Noreika, Ramunas Purtokas, Ingrida Lagzdinyte-Budnike, Vytautas Daniulaitis and Ruta Salickaite-Zukauskiene
Appl. Sci. 2022, 12(9), 4470; https://doi.org/10.3390/app12094470 - 28 Apr 2022
Cited by 9 | Viewed by 3403
Abstract
Global digitization trends and the application of high technology in the garment market are still too slow to integrate, despite the increasing demand for automated solutions. The main challenge is related to the extraction of garment information-general clothing descriptions and automatic dimensional extraction. [...] Read more.
Global digitization trends and the application of high technology in the garment market are still too slow to integrate, despite the increasing demand for automated solutions. The main challenge is related to the extraction of garment information-general clothing descriptions and automatic dimensional extraction. In this paper, we propose the garment measurement solution based on image processing technologies, which is divided into two phases, garment segmentation and key points extraction. UNet as a backbone network has been used for mask retrieval. Separate algorithms have been developed to identify both general and specific garment key points from which the dimensions of the garment can be calculated by determining the distances between them. Using this approach, we have resulted in an average 1.27 cm measurement error for the prediction of the basic measurements of blazers, 0.747 cm for dresses and 1.012 cm for skirts. Full article
(This article belongs to the Special Issue Advanced Digital Technologies for the Integration of Production and Maintenance)
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23 pages, 5406 KiB  
Article
A Non-Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain
by Ferdinando Chiacchio, Diego D’Urso, Ludovica Maria Oliveri, Alessia Spitaleri, Concetto Spampinato and Daniela Giordano
Appl. Sci. 2022, 12(8), 4019; https://doi.org/10.3390/app12084019 - 15 Apr 2022
Cited by 48 | Viewed by 11323
Abstract
Industry 4.0 is leading society into a new era characterized by smart communications between consumers and enterprises. While entertainment and fashion brands aim to consolidate their identities, increasing consumers’ participation in new, engaging, and immersive experiences, other industry sectors such as food and [...] Read more.
Industry 4.0 is leading society into a new era characterized by smart communications between consumers and enterprises. While entertainment and fashion brands aim to consolidate their identities, increasing consumers’ participation in new, engaging, and immersive experiences, other industry sectors such as food and drugs are called to adhere to stricter regulations to increase the quality assurance of their processes. The pharmaceutical industry is inherently one of the most regulated sectors because the safety, integrity, and conservation along the distribution network are the main pillars for guaranteeing the efficacy of drugs for the general public. Favoured by Industry 4.0 incentives, pharmaceutical serialization has become a must in the last few years and is now in place worldwide. In this paper, a decentralized solution based on non-fungible tokens (NFTs), which can improve the track and trace capability of the standard serialization process, is presented. Non-fungible tokens are minted in the blockchain and inherit all the advantages provided by this technology. As blockchain technology is becoming more and more popular, adoption of track and trace will increase tremendously. Focusing on the pharmaceutical industry’s use of track and trace, this paper presents the concepts and architectural elements necessary to support the non-fungible token solution, culminating in the presentation of a use case with a prototypical application. Full article
(This article belongs to the Special Issue Advanced Digital Technologies for the Integration of Production and Maintenance)
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27 pages, 39710 KiB  
Article
Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades
by Jonas Aust and Dirk Pons
Appl. Sci. 2022, 12(4), 2250; https://doi.org/10.3390/app12042250 - 21 Feb 2022
Cited by 24 | Viewed by 4790
Abstract
Background—Aircraft inspection is crucial for safe flight operations and is predominantly performed by human operators, who are unreliable, inconsistent, subjective, and prone to err. Thus, advanced technologies offer the potential to overcome those limitations and improve inspection quality. Method—This paper compares the performance [...] Read more.
Background—Aircraft inspection is crucial for safe flight operations and is predominantly performed by human operators, who are unreliable, inconsistent, subjective, and prone to err. Thus, advanced technologies offer the potential to overcome those limitations and improve inspection quality. Method—This paper compares the performance of human operators with image processing, artificial intelligence software and 3D scanning for different types of inspection. The results were statistically analysed in terms of inspection accuracy, consistency and time. Additionally, other factors relevant to operations were assessed using a SWOT and weighted factor analysis. Results—The results show that operators’ performance in screen-based inspection tasks was superior to inspection software due to their strong cognitive abilities, decision-making capabilities, versatility and adaptability to changing conditions. In part-based inspection however, 3D scanning outperformed the operator while being significantly slower. Overall, the strength of technological systems lies in their consistency, availability and unbiasedness. Conclusions—The performance of inspection software should improve to be reliably used in blade inspection. While 3D scanning showed the best results, it is not always technically feasible (e.g., in a borescope inspection) nor economically viable. This work provides a list of evaluation criteria beyond solely inspection performance that could be considered when comparing different inspection systems. Full article
(This article belongs to the Special Issue Advanced Digital Technologies for the Integration of Production and Maintenance)
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12 pages, 3181 KiB  
Article
Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning
by Jung-Sing Jwo, Ching-Sheng Lin, Cheng-Hsiung Lee, Li Zhang and Sin-Ming Huang
Appl. Sci. 2021, 11(17), 8243; https://doi.org/10.3390/app11178243 - 6 Sep 2021
Cited by 10 | Viewed by 2675
Abstract
Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of [...] Read more.
Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing. Full article
(This article belongs to the Special Issue Advanced Digital Technologies for the Integration of Production and Maintenance)
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