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Applied Sciences
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Reliability Modelling and Analysis for Complex Systems, Volume 2
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Reliability Modelling and Analysis for Complex Systems, Volume 2

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 3162

Special Issue Editor

Prof. Dr. Suk Joo Bae

E-Mail Website
Guest Editor
Department of Industrial Engineering, Hanyang University, Seoul 04763, Korea
Interests: reliability engineering; condition-based maintenance; degradation data analysis; manufacturing big-data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Reliability is an important issue during the development of a variety of systems or products (e.g., automobiles, airplanes, semiconductors, power plants) that ensures a system’s performance is maintained over a specified period of time in specific environments. As technology evolves, system complexity increases, and the evaluation of systems’ reliability—which remains an important area of research—has started to attract the attention of system engineers. To evaluate a system’s reliability within a relatively short period of time, engineers use physical or empirical test methods (e.g., accelerated life tests, accelerated degradation tests), then decide whether the system satisfies its requirements. Once the system is launched and used in the field, failure data or maintenance data are collected so that improvements can be made to maximize the system’s reliability and minimize operation costs. To this end, maintenance modelling aims to optimally balance the cost of maintenance and the reliability of complex systems. It provides a cost-driven mathematical basis to help keep the system of interest sustainable at a desired level.

At present, innovative tools for reliability analysis and decision-making in the design, operation, and maintenance of engineering systems are being developed for the safe, reliable, and effective operation of these systems. This Special Issue on “Reliability Modelling and Analysis for Complex Systems” presents a platform where researchers from academia and industry can present methodologies for coping with the uncertainties in reliability modeling and evaluation for complex systems through the use of concepts and various techniques, life tests, parametric or nonparametric methods, resampling methods (e.g., Monte Carlo simulation, bootstrapping), system reliability concepts, maintenance scheduling, Markov chain, stochastic processes, etc.

Prof. Dr. Suk Joo Bae
Guest Editor

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

  • system reliability
  • fuzzy reliability
  • warranty data analysis
  • maintainability and availability
  • accelerated life tests
  • degradation tests
  • diagnostics and prognostics
  • condition-based maintenance
  • software reliability and test
  • reliability redundancy allocation
  • fault tree analysis
  • big data and IoT applications for reliability improvement
  • markov chains
  • stochastic process
  • evolutionary algorithms
  • maintenance modelling, planning, scheduling and optimization
  • bayesian reliability
  • remaining useful life estimation
  • life cycle costs
  • machine learning and deep learning in maintenance modelling
  • reliability growth test
  • recurrent failure data analysis for repairable systems
  • safety and risk assessment

Published Papers (2 papers)

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Research

13 pages, 879 KiB  
Article
Long-Term Prediction of Crack Growth Using Deep Recurrent Neural Networks and Nonlinear Regression: A Comparison Study
by Salahuddin Muhammad Iqbal, Jun-Ryeol Park, Kyu-Il Jung, Jun-Seoung Lee and Dae-Ki Kang
Appl. Sci. 2022, 12(20), 10514; https://doi.org/10.3390/app122010514 - 18 Oct 2022
Viewed by 1236
Abstract
Cracks in a building can potentially result in financial and life losses. Thus, it is essential to predict when the crack growth is reaching a certain threshold, to prevent possible disaster. However, long-term prediction of the crack growth in newly built facilities or [...] Read more.
Cracks in a building can potentially result in financial and life losses. Thus, it is essential to predict when the crack growth is reaching a certain threshold, to prevent possible disaster. However, long-term prediction of the crack growth in newly built facilities or existing facilities with recently installed sensors is challenging because only the short-term crack sensor data are usually available in the aforementioned facilities. In contrast, we need to obtain equivalently long or longer crack sensor data to make an accurate long-term prediction. Against this background, this research aims to make a reasonable long-term estimation of crack growth within facilities that have crack sensor data with limited length. We show that deep recurrent neural networks such as LSTM suffer when the prediction’s interval is longer than the observed data points. We also observe a limitation of simple linear regression if there are abrupt changes in a dataset. We conclude that segmented nonlinear regression is suitable for this problem because of its advantage in splitting the data series into multiple segments, with the premise that there are sudden transitions in data. Full article
(This article belongs to the Special Issue Reliability Modelling and Analysis for Complex Systems, Volume 2)
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20 pages, 4031 KiB  
Article
Reportability Tool Design: Assessing Grouping Schemes for Strategic Decision Making in Maintenance Planning from a Stochastic Perspective
by Pablo Viveros, Nicolás Cárdenas Pantoja, Fredy Kristjanpoller and Rodrigo Mena
Appl. Sci. 2022, 12(11), 5386; https://doi.org/10.3390/app12115386 - 26 May 2022
Cited by 1 | Viewed by 1449
Abstract
In this article, we report on the design and implementation of a reportability tool using Microsoft Power BI embedded with Python script to assess opportunistic grouping schemes under a preventive maintenance policy. The reportability tool is based on specially developed indicators based on [...] Read more.
In this article, we report on the design and implementation of a reportability tool using Microsoft Power BI embedded with Python script to assess opportunistic grouping schemes under a preventive maintenance policy. The reportability tool is based on specially developed indicators based on current maintenance standards for better implementation and considers a formerly developed grouping strategy with poor embedded performance indicators as an implementation case for the tool. Performance indicators were carefully developed considering a stochastic perspective when possible; this enables decisions to be influenced by risk assessment under a costs view. Reporting is focused on six maintenance sub-functions, enabling the decision maker to easily assess performance of any maintenance process, thereby improving the quality of decisions. The developed tool is a step forward for grouping (or any scheduling scheme) strategies due to its flexibility to be implemented in almost any case, enabling comparison between different grouping algorithms. Full article
(This article belongs to the Special Issue Reliability Modelling and Analysis for Complex Systems, Volume 2)
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