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Special Issue "Reliability Modeling of Complex Systems in Materials and Devices"

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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".

Deadline for manuscript submissions: 10 January 2024 | Viewed by 6596

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Special Issue Editors

Dr. Cheng Qian

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Guest Editor

School of Reliability and Systems Engineering, Beihang University, Beijing, China
Interests: reliability simulation; physics of failure modeling; multi-physics simulation; system reliability modeling and analysis; lifetime prediction; uncertainty modeling and analysis; accelerated degradation method; SOC estimation of lithium-ion batteries; RUL and SOH prediction of lithium-ion batteries

Dr. Jiajie Fan

E-Mail Website
Guest Editor

Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
Interests: LED packaging and system integration; prognostics and health management; wide bandgap power electronics packaging and reliability modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Dezhen Yang

E-Mail Website
Guest Editor

School of Reliability and Systems Engineering, Beihang University, Beijing, China
Interests: reliability modeling; failure analysis; failure mitigation; digital twins for reliability; dependability of AI

Special Issue Information

Dear Colleagues,

As its name implies, “Complex Systems” are the systems consisting of multifold materials and components interacting with each other in complicated ways. They exist widely in all kinds of vital industries, including aerospace, civil, transport, energy, intelligent manufacturing and semiconductors. Driven by modern technologies, the complexity of those systems has increased dramatically, making reliability design and their optimization a great challenge in practical situations. On the other hand, numerous fantastic solutions on reliability analysis and evaluation have also emerged with the rapid advancement of technologies such as numerical simulations, big data, intelligence design, physics-of-failure modeling, and the multi-integration of these above technologies. By virtue of these methods, the reliability problems of complex systems could be tackled with great opportunities.

To extend the understanding of complex system reliability, reliability studies worldwide on advanced theory, models and algorithms for products at material, and component and system levels are particularly welcome in this Special Issue. The topics of interest include, but are not limited to:

Artificial intelligence based reliability modeling;
Physics-informed neural network for physics of failure;
Multi-physics and multi-scale simulation;
Reliability modeling, assessment and validation;
Design for reliability;
Prognostics and health management;
Digital twins;
Energy storage materials, devices, modules and systems;
Electronic packaging materials, devices and modules;
Wide bandgap semiconductor devices, modules and systems;
Electric products and systems;
Mechanical products and systems;
AI systems.

Dr. Cheng Qian
Dr. Jiajie Fan
Dr. Dezhen Yang
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. Materials 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

reliability
complex system
devices
materials
numerical simulations
physics-of-failure
lifetime prediction
prognostics and health management
artificial intelligence
digital twins

Published Papers (5 papers)

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Research

12 pages, 546 KiB

Open AccessArticle

Optimal Stopping Rules for Preventing Overloading of Multicomponent Systems

Andrzej Z. Grzybowski

Zbigniew Domański

and

Tomasz Derda

Materials 2023, 16(7), 2817; https://doi.org/10.3390/ma16072817 - 01 Apr 2023

Viewed by 572

Abstract

When random-strength components work as an interconnected parallel system, then its carrying capacity is random as well. In a case where such a multicomponent system is a subject of the stepwise-growing workload, some of its components fail and their loads are taken over by the ones that are intact. When the loading process is continued, the additional loads trigger consecutive failures that degrade the system, eventually leading to a complete failure. If the goal of the system is to carry as much load as possible, then the loading process should be continued, but no longer than until the loading capacity of the whole system is reached. On the other hand, with every additional load step, a failure of the system becomes more probable, as the carrying capacity is random and known solely through its probability distribution. In such cases, the decision on when to cease the loading process is not obvious. We introduce and analyse a minimal model of failure spreading in an array of progressively loaded pillars controlled by a decision-maker who stops the process when a required load is attained. We show how to construct an optimal stopping rule. Under some additional assumptions regarding the adopted loss function, it is argued that the optimal stopping rule is of the threshold type and it significantly depends on the shape of the load-step probability distribution. Full article

(This article belongs to the Special Issue Reliability Modeling of Complex Systems in Materials and Devices)

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9 pages, 1998 KiB

Open AccessArticle

Simulation of Far-Field Light Distribution of Micro-LED Based on Its Structural Parameters

and

Materials 2022, 15(24), 8854; https://doi.org/10.3390/ma15248854 - 12 Dec 2022

Cited by 1 | Viewed by 854

Abstract

To clarify how micro-LED far-field light distributions differ from Lambertian distributions owing to small-sized-structure effects, the light distribution of a micro-LED was simulated via the ray-tracing method in this study. Specifically, considering material absorption, far-field light distribution, and light-output efficiency, we studied micro-LEDs as a function of size. We found that the light distribution is the most uniform and the efficiency is the highest when the size is the smallest under certain conditions. Under other conditions, with increasing sapphire size, the luminous efficiency first increases and then decreases. The luminous efficiency is the highest when the thickness is 30 µm. Under certain other conditions, as the diameter of the micro-sphere structure on the sapphire increases, the luminous efficiency first increases and then decreases. Full article

(This article belongs to the Special Issue Reliability Modeling of Complex Systems in Materials and Devices)

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13 pages, 3224 KiB

Open AccessArticle

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

and

Materials 2022, 15(17), 5933; https://doi.org/10.3390/ma15175933 - 27 Aug 2022

Cited by 5 | Viewed by 1114

Abstract

Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery’s lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO₂ batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery’s lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery’s SOC accurately throughout the battery’s lifetime against varying initial SOCs. Full article

(This article belongs to the Special Issue Reliability Modeling of Complex Systems in Materials and Devices)

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22 pages, 4181 KiB

Open AccessArticle

A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution

and

Materials 2022, 15(9), 3331; https://doi.org/10.3390/ma15093331 - 06 May 2022

Cited by 10 | Viewed by 2280

Abstract

Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively. Full article

(This article belongs to the Special Issue Reliability Modeling of Complex Systems in Materials and Devices)

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16 pages, 4590 KiB

Open AccessArticle

Reliability Analysis of Critical Systems in A Fuel Booster Pump Using Advanced Simulation Techniques

and

Materials 2022, 15(6), 1989; https://doi.org/10.3390/ma15061989 - 08 Mar 2022

Viewed by 1153

Abstract

The fuel booster pump is one of the most vulnerable physical assets in an operating engine due to the harsh environmental and operational conditions. However, because of its high structural complexity and extreme operational conditions, the reliability design of the fuel booster pump becomes especially difficult, particularly by means of experiments. Thus, to overcome such a problem, advanced simulation techniques have become adequate solutions for the reliability assessment and analysis of a fuel booster pump at the design stage. In this paper, by considering the effects of the uncertainties of multiple design parameters, fatigue life distributions of the four key components (which are the sealing bolt, spline shaft, graphite ring, and inducer, respectively) in a fuel booster pump were first predicted by PoF-based reliability simulations. Then, through further sensitivity analysis on each key component, the design parameters most sensitive to the component mean fatigue life were detected from a total of 25 candidate parameters. These parameters include the “nominal diameter” and “preload” for the sealing bolt, “major and minor diameters of the small spline” for the spline shaft, “inside diameter” for the graphite ring, and “fuel pressure on the blade front surface” for the inducer, respectively. These sensitivity results were found to be in good agreement with the results from the qualitative cause analysis on each key component. Full article

(This article belongs to the Special Issue Reliability Modeling of Complex Systems in Materials and Devices)

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