Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems

Topic Information

Dear Colleagues,

There are various uncertainty sources affecting the design, manufacturing, and maintenance of complex engineering systems. In recent decades, uncertainty quantification has demonstrated great potential for scientifically analyzing how the uncertainties affect the performance of products. On the one hand, the uncertainty factors stemming from design, manufacturing, and operation are expected to be thoroughly quantified and considered when designing new products, which is the so-called design under uncertainty. On the other hand, the uncertainty sources contained in the manufacturing process and in the prediction of operational performance are expected to be comprehensively quantified and included for decision making under uncertainty during manufacturing and operation. The purpose of this topic is to present the latest advancements in the field of uncertainty quantification in design, manufacturing, and maintenance. The topic includes but is not limited to:

• Uncertainty quantification and reduction;
• Design under uncertainty;
• Decision making under uncertainty;
• Uncertainty modeling and analysis;
• Model calibration, verification, and validation;
• Risk and reliability analysis;
• Robust/reliability-based design optimization;
• Uncertainty-aware machine learning models;
• Uncertainty quantification in additive manufacturing;
• Confidence-based remaining useful life estimation;
• Uncertainty-aware diagnostics and prognostics;
• Uncertainty-aware battery management systems;
• Probabilistic and non-probabilistic approaches in complex engineering systems;
• Highly efficient uncertainty propagation techniques in complex engineering systems;
• Applications of uncertainty quantification in design, manufacturing, or maintenance.

Dr. Chen Jiang
Dr. Zhenzhong Chen
Dr. Xiaoke Li
Dr. Xiwen Cai
Dr. Zan Yang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.6	3.0	2014	22.3 Days	CHF 2400	Submit
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Batteries batteries	4.0	5.4	2015	17.7 Days	CHF 2700	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Journal of Marine Science and Engineering jmse	2.9	3.7	2013	15.4 Days	CHF 2600	Submit
Machines machines	2.6	2.1	2013	15.6 Days	CHF 2400	Submit
Mathematics mathematics	2.4	3.5	2013	16.9 Days	CHF 2600	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit

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Published Papers (9 papers)

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All Aerospace Applied Sciences Batteries Energies JMSE Machines Mathematics Sensors

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17 pages, 332 KiB  

Open AccessArticle

Estimation of the Resultant Expanded Uncertainty of the Output Quantities of the Measurement Chain Using the Discrete Wavelet Transform Algorithm

by Marian Kampik, Jerzy Roj and Łukasz Dróżdż

Appl. Sci. 2024, 14(9), 3691; https://doi.org/10.3390/app14093691 - 26 Apr 2024

Viewed by 165

Abstract

This paper discusses the role of the discrete wavelet transform algorithm in processing error signals present in the input quantities of the algorithm. In considering the error model of the measurement chain, the parameters of the error signals in the input quantities of [...] Read more.

This paper discusses the role of the discrete wavelet transform algorithm in processing error signals present in the input quantities of the algorithm. In considering the error model of the measurement chain, the parameters of the error signals in the input quantities of the wavelet transform algorithm are estimated. Subsequently, in accounting for the algorithm’s properties, the parameters of its output values are determined, and the resulting uncertainty values of the output quantities of the measurement chain are estimated. The interval reduction arithmetic method is employed in the calculations for estimating the expanded uncertainty. All findings were validated through measurements conducted using the implemented measurement chain. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

20 pages, 457 KiB  

Open AccessArticle

Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm

by Marian Kampik, Jerzy Roj and Łukasz Dróżdż

Appl. Sci. 2024, 14(8), 3461; https://doi.org/10.3390/app14083461 - 19 Apr 2024

Viewed by 241

Abstract

This paper presents an error model of a measurement chain containing a link that executes a discrete wavelet transform algorithm, which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities [...] Read more.

This paper presents an error model of a measurement chain containing a link that executes a discrete wavelet transform algorithm, which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both the deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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35 pages, 7701 KiB  

Open AccessArticle

Parameterized Reduced-Order Models for Probabilistic Analysis of Thermal Protection System Based on Proper Orthogonal Decomposition

by Kun Zhang, Jianyao Yao, Wenxiang Zhu, Zhifu Cao, Teng Li and Jianqiang Xin

Aerospace 2024, 11(4), 269; https://doi.org/10.3390/aerospace11040269 - 29 Mar 2024

Viewed by 469

Abstract

The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significant randomness, posing considerable challenges in engineering design and [...] Read more.

The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significant randomness, posing considerable challenges in engineering design and reliability assessment. Given that uncertain aerodynamic heating loads manifest as a stochastic field over time, conventional surrogate models, typically accepting scalar random variables as inputs, face limitations in modeling them. Consequently, this paper introduces an effective characterization approach utilizing proper orthogonal decomposition (POD) to represent the uncertainties of aerodynamic heating. The augmented snapshots matrix is used to reduce the dimension of the random field by the decoupling method of independently spatial and temporal bases. The random variables describing material properties and geometric thickness are also employed as inputs for probabilistic analyses. An uncoupled POD Gaussian process regression (UPOD-GPR) model is then established to achieve highly accurate solutions for transient heat conduction. The model takes random heat flux fields as inputs and thermal response fields as outputs. Using a typical multi-layer TPS and thermal structure as two examples, probabilistic analyses are conducted. The mean square relative error of a typical multi-layer TPS is less than 4%. For the thermal structure, the averaged absolute error of the radiation and insulation layer is less than 25 °C and 6 °C when the maximum reaches 1200 °C and 150 °C, respectively. This approach can provide accurate and rapid predictions of thermal responses for TPS and thermal structures throughout their entire operating time when furnished with input heat flux fields and structural parameters. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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19 pages, 8123 KiB  

Open AccessArticle

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

by Xing Shui, Zhijun Rong, Binbin Dan, Qiangjian He and Xin Yang

Machines 2024, 12(3), 184; https://doi.org/10.3390/machines12030184 - 12 Mar 2024

Viewed by 872

Abstract

Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making [...] Read more.

Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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19 pages, 3222 KiB  

Open AccessArticle

Bayesian Averaging Evaluation Method of Accelerated Degradation Testing Considering Model Uncertainty Based on Relative Entropy

by Tianji Zou, Wenbo Wu, Kai Liu, Ke Wang and Congmin Lv

Sensors 2024, 24(5), 1426; https://doi.org/10.3390/s24051426 - 22 Feb 2024

Viewed by 434

Abstract

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, [...] Read more.

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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22 pages, 4668 KiB  

Open AccessArticle

Cooperative Game-Based Digital Twin Drives Decision Making: Overall Framework, Basic Formalization and Application Case

by Fuwen Hu, Song Bi and Yuanzhi Zhu

Mathematics 2024, 12(2), 355; https://doi.org/10.3390/math12020355 - 22 Jan 2024

Viewed by 868

Abstract

The emerging progress brought about by Industry 4.0 generates great opportunities for better decision making to cope with increasingly uncertain and complex industrial production. From the perspective of game theory, methods based on computational simulations and methods based on physical entities have their [...] Read more.

The emerging progress brought about by Industry 4.0 generates great opportunities for better decision making to cope with increasingly uncertain and complex industrial production. From the perspective of game theory, methods based on computational simulations and methods based on physical entities have their intrinsic drawbacks, such as partially accessible information, uncontrollable uncertainty and limitations of sample data. However, an insight that inspired us was that the digital twin modeling method induced interactive environments to allow decision makers to cooperatively learn from the immediate feedback from both cyberspace and physical spaces. To this end, a new decision-making method was put forward using game theory to autonomously ally the digital twin models in cyberspace with their physical counterparts in the real world. Firstly, the overall framework and basic formalization of the cooperative game-based decision making are presented, which used the negotiation objectives, alliance rules and negotiation strategy to ally the planning agents from the physical entities with the planning agents from the virtual simulations. Secondly, taking the assembly planning of large-scale composite skins as a proof of concept, a cooperative game prototype system was developed to marry the physical assembly-commissioning system with the virtual assembly-commissioning system. Finally, the experimental work clearly indicated that the coalitional game-based twinning method could make the decision making of composite assembly not only predictable but reliable and help to avoid stress concentration and secondary damage and achieve high-precision assembly. Obviously, this decision-making methodology that integrates the physical players and their digital twins into the game space can help them take full advantage of each other and make up for their intrinsic drawbacks, and it preliminarily demonstrates great potential to revolutionize the traditional decision-making methodology. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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27 pages, 15861 KiB  

Open AccessArticle

A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles

by Shancheng Tang, Ying Zhang, Zicheng Jin, Jianhui Lu, Heng Li and Jiqing Yang

Appl. Sci. 2024, 14(1), 386; https://doi.org/10.3390/app14010386 - 31 Dec 2023

Viewed by 774

Abstract

The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, the normal texture of the [...] Read more.

The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, the normal texture of the aluminum profile surface presents non-uniform and non-periodic features, and this irregular distribution makes it difficult for classical reconstruction networks to accurately reconstruct the normal features, resulting in low performance of related unsupervised detection methods. Aiming at such problems, a feature-oriented reconstruction method of unsupervised surface-defect detection method for aluminum profiles is proposed. The aluminum profile image preprocessing stage uses techniques such as boundary extraction, background removal, and data normalization to process the original image and extract the image of the main part of the aluminum profile, which reduces the influence of irrelevant data features on the algorithm. The essential features learning stage precedes the feature-optimization module to eliminate the texture interference of the irregular distribution of the aluminum profile surface, and image blocks of the area images are reconstructed one by one to extract the features through the mask. The defect-detection stage compares the structural similarity of the feature images before and after the reconstruction, and comprehensively determines the detection results. The experimental results improve detection precision by 1.4% and the F1 value by 1.2% over the existing unsupervised methods, proving the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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38 pages, 1728 KiB  

Open AccessArticle

Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability

by Xuemiao Cui, Jiping Lu and Yafeng Han

Sensors 2024, 24(1), 165; https://doi.org/10.3390/s24010165 - 27 Dec 2023

Viewed by 646

Abstract

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, [...] Read more.

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, current studies only consider these three sources of variability partially. To this end, this paper presents a two-phase nonlinear degradation model with three-source variability based on the nonlinear Wiener process. Then, the approximate analytical solution of the RUL with three-source variability is derived under the concept of the first passage time (FPT). For better implementation, the offline model parameter estimation is conducted by the maximum likelihood estimation (MLE), and the Bayesian rule in conjunction with the Kalman filtering (KF) algorithm are utilized for the online model updating. Finally, the effectiveness of the proposed approach is validated through a numerical example and a practical case study of the capacitor degradation data. The results show that it is necessary to incorporate three-source variability simultaneously into the RUL prediction of the two-phase nonlinear degrading systems. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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17 pages, 6198 KiB  

Open AccessArticle

Mechanical Performance of a Node-Reinforced Body-Centered Cubic Lattice Structure: An Equal-Strength Concept Design

by Zeliang Liu, Rui Zhao, Chenglin Tao, Yuan Wang and Xi Liang

Aerospace 2024, 11(1), 4; https://doi.org/10.3390/aerospace11010004 - 19 Dec 2023

Cited by 1 | Viewed by 957

Abstract

Lattice structures are characterized by a light weight, high strength, and high stiffness, and have a wide range of applications in the aerospace field. Node stress concentration is a key factor affecting the mechanical performance of lattice structures. In this paper, a new [...] Read more.

Lattice structures are characterized by a light weight, high strength, and high stiffness, and have a wide range of applications in the aerospace field. Node stress concentration is a key factor affecting the mechanical performance of lattice structures. In this paper, a new equal-strength body-centered cubic (ES-BCC) lattice structure was additively manufactured using 316L stainless steel via selective laser melting (SLM). The results of a mechanical compression test and finite element analysis revealed that the failure location of the ES-BCC structure changed from the nodes to the center of the struts. At the same density, the energy absorption, elastic modulus, and yield strength of the ES-BCC structure increased by 11.89%, 61.80%, and 53.72% compared to the BCC structure, respectively. Furthermore, the change in angle of the ES-BCC structure achieves significant changes in strength, stiffness, and energy absorption to meet different design requirements and engineering applications. The equal-strength concept design can be applied as a general design method to the design of other lightweight energy-absorbing lattice structures. Full article

(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)

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