Reliable Systems Engineering: Design, Implementation and Maintenance

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 3332

Special Issue Editors

Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, Italy
Interests: robotics; mechanical design; automatic inspection
Special Issues, Collections and Topics in MDPI journals
Department of Mechatronics and Precision Mechanics, University "Politehnica" of Bucharest, Splaiul Independentei 313, Sector 6, 060042 Bucharest, Romania
Interests: biomedical equipment; distributed control; finite element analysis; kinematics; laser applications in medicine; legged locomotion; light sources; permanent magnets; piezoelectric actuators; temperatur
Operations and Supply Chain Division, NITIE Mumbai, Maharashtra 400087, India
Interests: operations research; multi-objective; supply chain; inventory management; supply and management; industrial engineering; production planning; manufacturing; production systems; manufacturing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent scientific and technological evolutions are leading to a better-connected world. In this context, production systems engineering must be flexible, reliable, and correspond to the constant more efficient demand from clients of products and services. Aspects related with design, implementation, and maintenance of those systems are crucial for the expected behavior from respective users. This Special Issue considers all of the steps in the developmental process, from theoretical study and the development of innovative solutions to the implementation and maintenance of such systems during their cycle of life, contributing to a more sustainable and better world that is concerned with sustainability and efficiency of all involved stakeholders in this complex achievement.

The scope of this Special Issue is closely associated with that of the ISPEM’2023 conference. This conference and Special Issue are to present the current innovations and engineering achievements of scientists and industrial practitioners in the thematic areas described above.

Topics of interest include but are not limited to the following:

  • Production Systems Management and Maintenance;
  • Industry 4.0 and Industry 5.0;
  • Modelling, Simulation, and Design;
  • Production Planning and Scheduling;
  • Maintenance Planning and Scheduling;
  • Intelligent Methods in Production and Maintenance;
  • Manufacturing Technology and System Engineering;
  • Supply Chain Management;
  • Reliability and Risk Assessment;
  • Control and Supervision;
  • Robotics;
  • Automation Measuring Systems and Sensors;
  • Cyber-Physical Systems;
  • Cloud and Distributed Manufacturing;
  • Human Aspects in Industry;
  • Advanced Aerospace Material and Composites;
  • Machining/Forming of Advanced Materials.

Dr. Jose Machado
Dr. Katarzyna Antosz
Dr. Erika Ottaviano
Dr. Bogdan Gramescu
Dr. Vijaya Kumar Manupati
Dr. Anna Burduk
Guest Editors

Manuscript Submission Information

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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.

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Keywords

  • reliable systems engineering
  • industry 4.0
  • cyber-physical systems
  • iot mechatronics design
  • smart maintenance risk management

Published Papers (2 papers)

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Research

12 pages, 1840 KiB  
Article
Intelligent Model for the Reliability of the Non-Intrusive Continuous Sensors Used for the Detection of Fouling-Layer in Heat Exchanger System
Appl. Sci. 2023, 13(5), 3028; https://doi.org/10.3390/app13053028 - 27 Feb 2023
Viewed by 841
Abstract
Faults in this sensor must be detected on time to ensure the functionality of the entire system’s architecture and to maintain system balance, which will keep false positive rates low during the system’s operational period. False positives reduce diagnostic confidence and necessitate unnecessary [...] Read more.
Faults in this sensor must be detected on time to ensure the functionality of the entire system’s architecture and to maintain system balance, which will keep false positive rates low during the system’s operational period. False positives reduce diagnostic confidence and necessitate unnecessary and costly mitigation actions, lowering system productivity. It is on this basis that this study proposes a clustering model algorithm (K-mean clustering) to investigate and manage the reliability and performance of the sensors. The results from the implementation of the K-mean clustering method show that the running of the algorithm fits the model correctly, both for the training of the dataset and for the prediction of the cluster in each of the datasets considered. A reasonable grouping was found for the two and three clusters considered, which are represented by the colors (blue, orange, and green). These colors indicate the fault state, non-fault state, normal state, and abnormal state of the non-intrusive continuous sensor. The simulated results show the fault state in the blue region and the non-fault state in the orange region for the two clusters, while the normal state is in the blue region and the abnormal state is in the orange and green regions for the three clusters considered. Full article
(This article belongs to the Special Issue Reliable Systems Engineering: Design, Implementation and Maintenance)
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12 pages, 2874 KiB  
Article
Supply Sequence Modelling Using Hidden Markov Models
Appl. Sci. 2023, 13(1), 231; https://doi.org/10.3390/app13010231 - 24 Dec 2022
Cited by 5 | Viewed by 1745
Abstract
Logistics processes, their effective planning as well as proper management and effective implementation are of key importance in an enterprise. This article analyzes the process of supplying raw materials necessary for the implementation of production tasks. The specificity of the examined waste processing [...] Read more.
Logistics processes, their effective planning as well as proper management and effective implementation are of key importance in an enterprise. This article analyzes the process of supplying raw materials necessary for the implementation of production tasks. The specificity of the examined waste processing company requires the knowledge about the size of potential deliveries because the delivered waste must be properly managed and stored due to its toxicity to the natural environment. In the article, hidden Markov models were used to assess the level of supply. They are a statistical modeling tool used to analyze and predict the phenomena of a sequence of events. It is not always possible to provide sufficiently reliable information with the existing classical methods in this regard. Therefore, the article proposes modeling techniques with the help of stochastic processes. In hidden Markov models, the system is represented as a Markov process with states that are invisible to the observer but with a visible output (observation) that is a random state function. In the article, the distribution of outputs from the hidden states is defined by a polynomial distribution. Full article
(This article belongs to the Special Issue Reliable Systems Engineering: Design, Implementation and Maintenance)
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