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Mathematics
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Recent Advances in Modeling for Reliability Analysis
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Recent Advances in Modeling for Reliability Analysis

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

Deadline for manuscript submissions: closed (1 August 2020) | Viewed by 13908

Special Issue Editors

Prof. Dr. Willem D van Driel

E-Mail Website
Guest Editor
Electronic Components, Technology and Materials (ECTM) Group, Department of Microelectronics, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands
Interests: reliability; systems; integration; virtual prototyping; statistics; health monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. R.T.H. Rongen

E-Mail Website
Guest Editor
Regional Quality Center Europe, NXP Semiconductors, 6534 Eindhoven, The Netherlands
Interests: reliability physics; robustness validation; mission profiles; reliability prediction and modeling; health monitoring

Special Issue Information

Dear Colleagues,

When creating new (integrated) electronic functionalities and/or increasing performance, the concerns of reliability and functional safety should be accounted for right from the start of development. This avoids wrong choices that otherwise may lead to costly and time-consuming repetitions of several development steps or even major parts of the development. In the worst case, unreliable products could enter the market with dramatic consequences for customers and suppliers. The main challenges in the electronic industry are related to:

  • Continuous growth in the number, complexity, and diversity of the functional features, of the devices and components integrated, as well as of the technologies and the materials involved in each product;
  • Increase in the reliability and safety level to be achieved by the products, which will simultaneously and more frequently be deployed in ever harsher environments;
  • Decrease in time-to-market and cost-per-product due to stronger global competition;
  • Higher complexity and depth of the supply chain raises the risk of hidden quality and reliability issues, in particular at the interface between electronic components, the system in which they are built-in and the application in which the system is used.

In this Special Issue, we invite you to present your advances in the reliability domain that take into the account the above-mentioned challenges.

Prof. Dr. Willem van Driel
Dr. René T.H. Rongen
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.

Keywords

  • knowledge-based qualification
  • acceleration models
  • failure modes
  • structural similarity
  • failure analysis

Published Papers (4 papers)

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Research

14 pages, 918 KiB  
Article
Software Reliability for Agile Testing
by Willem Dirk van Driel, Jan Willem Bikker, Matthijs Tijink and Alessandro Di Bucchianico
Mathematics 2020, 8(5), 791; https://0-doi-org.brum.beds.ac.uk/10.3390/math8050791 - 13 May 2020
Cited by 8 | Viewed by 3359
Abstract
It is known that quantitative measures for the reliability of software systems can be derived from software reliability models, and, as such, support the product development process. Over the past four decades, research activities in this area have been performed. As a result, [...] Read more.
It is known that quantitative measures for the reliability of software systems can be derived from software reliability models, and, as such, support the product development process. Over the past four decades, research activities in this area have been performed. As a result, many software reliability models have been proposed. It was shown that, once these models reach a certain level of convergence, it can enable the developer to release the software and stop software testing accordingly. Criteria to determine the optimal testing time include the number of remaining errors, failure rate, reliability requirements, or total system cost. In this paper, we present our results in predicting the reliability of software for agile testing environments. We seek to model this way of working by extending the Jelinski–Moranda model to a “stack” of feature-specific models, assuming that the bugs are labeled with the features they belong to. In order to demonstrate the extended model, two use cases are presented. The questions to be answered in these two cases are: how many software bugs remain in the software and should one decide to stop testing the software? Full article
(This article belongs to the Special Issue Recent Advances in Modeling for Reliability Analysis)
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18 pages, 1767 KiB  
Article
A Statistical Investigation into Assembly Tolerances of Gradient Field Magnetic Angle Sensors with Hall Plates
by Serkan Ergun, Udo Ausserlechner, Michael Holliber, Wolfgang Granig and Hubert Zangl
Mathematics 2019, 7(10), 968; https://0-doi-org.brum.beds.ac.uk/10.3390/math7100968 - 14 Oct 2019
Cited by 1 | Viewed by 2664
Abstract
Magnetic field-based sensors are used for reliable applications in automotive and aerospace industry because of their robustness. The electrification of powertrains and propulsion requires gradiometric sensing principles, because they suppress ubiquitous electromagnetic disturbances very efficiently. A prominent solution for the mass market uses [...] Read more.
Magnetic field-based sensors are used for reliable applications in automotive and aerospace industry because of their robustness. The electrification of powertrains and propulsion requires gradiometric sensing principles, because they suppress ubiquitous electromagnetic disturbances very efficiently. A prominent solution for the mass market uses a small permanent magnet attached to the end of a rotatable shaft and a small sensor chip with four Hall plates placed on the rotation axis and ahead of the magnet. Small misplacements of chip and magnet lead to errors in the detected angle of the shaft. The lateral position errors and tilts of the magnet and the chip give eight degrees of freedom (DoF). This large number of DoF and the nonlinearity of the system obscure the view on how to optimize such angle sensor systems. Therefore, this work presents a statistical description of angle errors caused by assembly tolerances. Probability distributions of angle errors are given and marked differences to Gaussian distributions are shown. The influence of spacing between sensor and magnet and the dominant influence of the shape of the magnet are clarified. The results obtained by numerical computations are in excellent agreement to recently published analytical theories. This work gives evident conclusions for statistical optimizations of such angle sensor systems. Full article
(This article belongs to the Special Issue Recent Advances in Modeling for Reliability Analysis)
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20 pages, 611 KiB  
Article
A Novel FMEA Model Based on Rough BWM and Rough TOPSIS-AL for Risk Assessment
by Tai-Wu Chang, Huai-Wei Lo, Kai-Ying Chen and James J. H. Liou
Mathematics 2019, 7(10), 874; https://0-doi-org.brum.beds.ac.uk/10.3390/math7100874 - 20 Sep 2019
Cited by 49 | Viewed by 4109
Abstract
Failure mode and effects analysis (FMEA) is a risk assessment method that effectively diagnoses a product’s potential failure modes. It is based on expert experience and investigation to determine the potential failure modes of the system or product to develop improvement strategies to [...] Read more.
Failure mode and effects analysis (FMEA) is a risk assessment method that effectively diagnoses a product’s potential failure modes. It is based on expert experience and investigation to determine the potential failure modes of the system or product to develop improvement strategies to reduce the risk of failures. However, the traditional FMEA has many shortcomings that were proposed by many studies. This study proposes a hybrid FMEA and multi-attribute decision-making (MADM) model to establish an evaluation framework, combining the rough best worst method (R-BWM) and rough technique for order preference by similarity to an ideal solution technique (R-TOPSIS) to determine the improvement order of failure modes. In addition, this study adds the concept of aspiration level to R-TOPSIS technology (called R-TOPSIS-AL), which not only optimizes the reliability of the TOPSIS calculation program, but also obtains more potential information. This study then demonstrates the effectiveness and robustness of the proposed model through a multinational audio equipment manufacturing company. The results show that the proposed model can overcome many shortcomings of traditional FMEA, and effectively assist decision-makers and research and development (R&D) departments in improving the reliability of products. Full article
(This article belongs to the Special Issue Recent Advances in Modeling for Reliability Analysis)
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17 pages, 1881 KiB  
Article
Risk Assessment for Failure Mode and Effects Analysis Using the Bonferroni Mean and TODIM Method
by Jianghong Zhu, Bin Shuai, Rui Wang and Kwai-Sang Chin
Mathematics 2019, 7(6), 536; https://0-doi-org.brum.beds.ac.uk/10.3390/math7060536 - 12 Jun 2019
Cited by 25 | Viewed by 3262
Abstract
As a safety and reliability analysis technique, failure mode and effects analysis (FMEA) has been used extensively in several industries for the identification and elimination of known and potential failures. However, some shortcomings associated with the FMEA method have limited its applicability. This [...] Read more.
As a safety and reliability analysis technique, failure mode and effects analysis (FMEA) has been used extensively in several industries for the identification and elimination of known and potential failures. However, some shortcomings associated with the FMEA method have limited its applicability. This study aims at presenting a comprehensive FMEA model that could efficiently handle the preference interdependence and psychological behavior of experts in the process of failure modes ranking. In this model, a linguistic variable expressed by the interval-valued Pythagorean fuzzy number (IVPFN) is utilized by experts to provide preference information with regard to failure modes’ evaluation and risk factors’ weight. Then, to depict the interdependent relationships between experts’ preferences, the Bonferroni mean operator is extended to IVPFN to aggregate the experts’ preference. Subsequently, an extended TODIM approach in which the dominance degree of failure modes is calculated by grey relational analysis is utilized to determine the risk priority of failure modes. Finally, a practical example concerning the risk assessment of a nuclear reheat valve system is provided to demonstrate the effectiveness and feasibility of the presented method. In addition, a sensitivity analysis and comparison analysis are conducted, and the results show that the preference interdependence and psychological behavior of experts have an important effect on the risk priority of failure modes. Full article
(This article belongs to the Special Issue Recent Advances in Modeling for Reliability Analysis)
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