Article
Engineering, Mechanical
John Hickey, Robin Langley
Summary: This paper discusses the importance of separating aleatory and epistemic uncertainties, proposes alternative metrics based on the epistemic probability of aleatory probabilities, and illustrates them through two engineering problems.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Amin Hariri-Ardebili, Farhad Pourkamali-Anaraki
Summary: Quantifying the impact of uncertainty in material properties and ground motion records on structural response is crucial in performance-based earthquake engineering. This paper proposes an algorithm that combines different realizations of hybrid epistemic-aleatory RVs, utilizes matrix completion methods, and applies sampling techniques and regression analysis for estimating structural responses. The algorithm is successfully applied to a complex tower, showing its effectiveness in estimating responses and providing recommendations for choosing the optimal percentage of initial simulations.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Multidisciplinary
Zhiheng Wang, Roger Ghanem
Summary: This paper introduces an extended polynomial chaos formalism for addressing epistemic uncertainties and a new framework for evaluating sensitivities and variations of output probability density functions to uncertainty in probabilistic models of input variables. By combining aleatory and epistemic uncertainties in a unified treatment, a PCE-based approach is developed to evaluate sensitivities of outputs to input parameters efficiently. The integration of epistemic uncertainties within the PCE framework provides a computationally efficient paradigm for propagation and sensitivity evaluation.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Ecology
Jody R. Reimer, Frederick R. Adler, Kenneth M. Golden, Akil Narayan
Summary: Uncertainty in parameters in ecological models can be incorporated by treating parameters as random variables with distributions. Recent advances in uncertainty quantification methods provide new approaches for analyzing models with random parameters. Modelling key parameters as random variables changes the characteristics of the model. The computational efficiency of polynomial chaos methods helps in better predicting and synthesizing models with data.
Article
Engineering, Aerospace
Jinwu LI, Chao Jiang
Summary: This paper proposes a novel model called the imprecise stochastic process model for handling the dynamic uncertainty in real-world problems. It introduces the definition and categorization of the P-box-based imprecise stochastic process and presents a time-variant reliability analysis approach. The effectiveness of the proposed method is verified through numerical examples.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Article
Engineering, Aerospace
Zhiheng Wang, Roger Ghanem
Summary: We introduce a new stochastic optimal control framework that considers various uncertainties in reentry trajectory planning. The optimal trajectory control problem is formulated using an indirect method to minimize a functional objective related to the final vehicle speed. Uncertain input parameters are modeled as aleatory random variables, while the statistical parameters of these random variables are also random variables themselves. Using an extended polynomial chaos expansion (EPCE) formalism, both parametric and model uncertainties are simultaneously propagated. Various metrics are described to evaluate response statistics and provide insights for robust decision making.
Article
Engineering, Civil
Akshay Kumar, A. S. Balu
Summary: It is crucial to consider uncertainties in structures during analysis and design. These uncertainties can be categorized as aleatory and epistemic based on their sources. When the information about the system is partially available, methods like combinatorial approach, interval analysis, and universal grey theory are commonly used. This paper proposes a modified method to overcome the limitations of traditional universal grey theory.
Article
Engineering, Industrial
Ying Chen, Yanfang Wang, Shumin Li, Rui Kang
Summary: This paper analyzes and quantifies the hybrid uncertainty of DCFP system, and proposes a combinational algorithm to solve it. Taking the control system of an aircraft as a case, the reliability of the system is evaluated and the effects of key parameters on system reliability are discussed.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Seyed Ashkan Zarghami, Jantanee Dumrak
Summary: This paper fills the gap in uncertainty analysis of resource acquisition in project management by taking a success-oriented view and constructing Reliability Block Diagram (RBD). A new schedule sensitivity index is proposed to account for uncertainty in time duration and resource acquisition, demonstrating better performance in evaluating the relative importance of activities in stochastic project networks.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Civil
Marc Fina, Celine Lauff, Matthias G. R. Faes, Marcos A. Valdebenito, Werner Wagner, Steffen Freitag
Summary: This paper proposes a framework to calculate the bounds on failure probability of linear structural systems affected by random and interval variables. The framework uses the maximum standard deviation of the structural response as a proxy for detecting the crisp values of interval parameters and obtaining failure probability bounds. The proposed approach is applicable to linear structural systems with aleatoric and epistemic uncertainty and Gaussian loading.
Article
Engineering, Mechanical
R. Allahvirdizadeh, A. Andersson, R. Karoumi
Summary: The operational safety of high-speed trains on ballasted bridges relies on preventing ballast destabilization. This study explores the impact of epistemic uncertainties on the system using ISRA. Neglecting these uncertainties can lead to overestimation of permissible train speeds and reduced system safety.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Computer Science, Interdisciplinary Applications
Shuang Zhou, Jianguo Zhang, Qingyuan Zhang, Meilin Wen
Summary: This paper proposes a methodology of hybrid reliability analysis and optimization based on chance theory to control aleatory and epistemic uncertainties in the preliminary design phase of engineering structures. It uses random variables to describe aleatory uncertainty and uncertain variables to quantify epistemic uncertainty. The chance measure and chance reliability indicator (CRI) are introduced to model structural reliability in the presence of hybrid uncertainty. Two CRI estimation methods and two solving strategies are developed for mixed reliability assessment and design optimization. The performance and feasibility of the proposed analysis model and solution technique are verified through four engineering applications.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Thermodynamics
Diederik Coppitters, Ward De Paepe, Francesco Contino
Summary: This study considers the effects of limited information on the natural variability through probability-boxes, finding the least-sensitive designs to natural variability and effective actions to reduce the effects of limited information. The photovoltaic-battery-heat pump configuration achieves higher robustness towards aleatory uncertainty, while clarifying the grid electricity contract and adopting specific energy demand profiles are key actions to determine the true-but-unknown performance and robustness of the optimized designs.
Article
Management
Daniel J. Walters, Gulden Ulkumen, David Tannenbaum, Carsten Erner, Craig R. Fox
Summary: We find that investor behavior is influenced by two dimensions of subjective uncertainty: epistemic uncertainty (missing knowledge, skill, or information) and aleatory uncertainty (chance or stochastic processes). Investors who perceive higher epistemic uncertainty are more likely to seek expert guidance and use available information, while those perceiving higher aleatory uncertainty are more inclined to diversify and their risk preferences better predict their investment choices. We also demonstrate that the presentation format of historical information can influence uncertainty attributions, with charts of absolute stock prices promoting perceptions of epistemicness and willingness to pay for financial advice, while charts of price changes promoting perceptions of aleatoriness and a tendency to diversify.
MANAGEMENT SCIENCE
(2023)
Article
Construction & Building Technology
Jose Caceres, Danilo Gonzalez, Taotao Zhou, Enrique Lopez Droguett
Summary: A probabilistic Bayesian recurrent neural network (RNN) is proposed to address epistemic and aleatory uncertainties in RUL prognostics, utilizing Bayesian RNN layers and a probabilistic output layer parameterized by a Gaussian distribution. Experimental results demonstrate the promising performance and robustness of this model in RUL prognostics.
STRUCTURAL CONTROL & HEALTH MONITORING
(2021)
Article
Construction & Building Technology
Bo Fu, Xinxin Wei, Jin Chen, Sifeng Bi
Summary: The shear lag effect is a vital mechanical characteristic of structures with thin-walled box sections, and it has significant effects on the vibration response of footbridges. It is important to consider the factors that influence the natural frequencies and pedestrian-induced vibrations of the structures, as well as design reliable vibration mitigation measures.
STRUCTURAL ENGINEERING INTERNATIONAL
(2023)
Article
Engineering, Multidisciplinary
Masaru Kitahara, Chao Dang, Michael Beer
Summary: This paper proposes a Bayesian updating approach called parallel Bayesian optimization and quadrature (PBOQ). It applies Gaussian process priors and explores a constant c in BUS through parallel infill sampling strategy. The proposed approach effectively reduces computational burden of model updating by leveraging prior knowledge and parallel computing. Numerical examples are used to demonstrate its potential benefits and advocate a coherent Bayesian fashion for BUS analysis.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Mechanical
Zhiqiang Wan, Jianbing Chen, Weifeng Tao, Pengfei Wei, Michael Beer, Zhongming Jiang
Summary: This paper addresses the issue of metamodelling of nonlinear stochastic dynamical systems with multiple input uncertainties. It proposes a feature mapping strategy to handle the curse of dimensionality problem caused by high-dimensional input uncertainties. The paper introduces methods for extracting feature spaces of outputs and inputs, and completes the process of metamodelling using polynomial chaos expansion combined with Kriging. The accuracy and efficiency of the proposed method are demonstrated through experiments on several benchmarks.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Jiang Mo, Wang-Ji Yan, Ka-Veng Yuen, Michael Beer
Summary: Interval arithmetic is a powerful tool for updating structural models with uncertain-but-bounded parameters. However, the complexity and computational burden of interval model updating hinder its practical application. This study proposes an efficient inner-outer decoupling scheme to address this issue. The scheme decomposes the mathematical operation of interval model updating into two layers, namely, uncertainty propagation and interval optimization, to improve search efficiency and convergence rate.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
P. Ni, V. C. Fragkoulis, F. Kong, I. P. Mitseas, M. Beer
Summary: This paper proposes a new technique for determining the response of multi-degree-of-freedom nonlinear systems with singular parameter matrices subject to combined deterministic and non-stationary stochastic excitation. The system response is decomposed into deterministic and stochastic components, corresponding to the two components of the excitation. Two sets of differential equations are formulated and solved simultaneously to compute the system response. The efficiency of the proposed technique is demonstrated by numerical examples involving a vibration energy harvesting device and a structural nonlinear system.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Yongchao Zhang, Zhaohui Ren, Ke Feng, Kun Yu, Michael Beer, Zheng Liu
Summary: Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge from a labeled source domain to a new unlabeled target domain. Existing methods often assume prior knowledge of fault modes in the target domain, which is rare in engineering practice. This study proposes a source-free domain adaptation method that can handle cross-domain fault diagnosis scenarios without source data and explicit assumptions about target fault modes.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Marco Behrendt, Matthias G. R. Faes, Marcos A. Valdebenito, Michael Beer
Summary: In engineering, the modelling of environmental processes is essential for designing structures safely and determining the reliability of existing structures. This work focuses on situations where data is limited and it is not feasible to derive reliable statistics. The proposed approach uses a radial basis function network to generate basis functions that enclose the data, resulting in an interval-based power spectral density (PSD) function. The applicability of this imprecise PSD model is demonstrated with recorded earthquake ground motions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Industrial
Pengfei Wei, Yu Zheng, Jiangfeng Fu, Yuannan Xu, Weikai Gao
Summary: The combination of active learning with surrogate model has been proven to improve the efficiency of structural reliability analysis. This paper proposes a new acquisition function, called Expected Integrated Error Reduction (EIER), for active learning of failure probability. The superiority of the proposed improvements is demonstrated with numerical and engineering examples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Yang Zhang, Jun Xu, Michael Beer
Summary: This paper proposes a single-loop approach for time-variant reliability evaluation based on a decoupling strategy and probability distribution reconstruction. The proposed method allows capturing the reliability at a specified time instant by performing time-invariant reliability analysis only once. The method employs the expansion optimal linear estimation, decoupling strategy, Box-Cox transformation, and maximum entropy method to derive the probability distribution of the equivalent extreme value limit state function and compute the time-variant failure probability efficiently.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Mechanical
Zhibao Zheng, Michael Beer, Udo Nackenhorst
Summary: This paper presents an efficient multi-fidelity scheme to simulate multi-dimensional non-Gaussian random fields. Two numerical algorithms are proposed to generate random samples that satisfy the target covariance function and marginal distribution. The computational effort is reduced by using Karhunen-Loeve expansion.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Industrial
Danko J. Jerez, M. Chwala, Hector A. Jensen, Michael Beer
Summary: This paper proposes a framework for designing optimal borehole configurations for shallow foundation systems under undrained soil conditions. It minimizes the standard deviations of the bearing capacity to ensure performance. The method adopts a random failure mechanism for evaluating random bearing capacity and provides sensitivity information of the selected performance measure.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Mechanical
Xu-Yang Cao, De-Cheng Feng, Michael Beer
Summary: With the development of performance-based earthquake engineering, the risk-informed assessment framework has gained recognition worldwide, particularly the probability seismic fragility analysis. Researchers are exploring non-parametric approaches to express intrinsic fragility without distribution assumptions, while also considering calculation efficiency and non-stationary stochastic responses. This paper proposes a kernel density estimation-based non-parametric cloud approach for efficient seismic fragility estimation and demonstrates its effectiveness through an application example. The findings provide insights for the development of non-parametric seismic fragility approaches.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Sifeng Bi, Michael Beer, Scott Cogan, John Mottershead
Summary: This paper introduces the theoretic framework of stochastic model updating, covering critical aspects such as model parameterisation, sensitivity analysis, surrogate modelling, test-analysis correlation, and parameter calibration. It emphasizes on uncertainty analysis and extends model updating to the stochastic domain by quantifying uncertainties and treating model parameters as random variables with imprecise probabilities. The paper elaborates on two key aspects, forward uncertainty propagation and inverse parameter calibration, and introduces techniques such as P-box propagation, statistical distance-based metrics, Markov chain Monte Carlo sampling, and Bayesian updating. The technical framework is demonstrated through solving challenges and benchmark testbeds, encouraging readers to reproduce the results and providing further directions for stochastic model updating with uncertainty treatment perspectives.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Industrial
Jingwen Song, Yifan Cui, Pengfei Wei, Marcos A. Valdebenito, Weihong Zhang
Summary: Estimating design points accurately is crucial for reliability analysis and reliability-based design optimization. This research proposes two acquisition functions and develops a Constrained Bayesian Optimization method for actively learning high accuracy and globally converging design points. Additionally, an improved algorithm is introduced for adaptively learning design points far away from the origin.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Multidisciplinary
Fangqi Hong, Pengfei Wei, Jingwen Song, Marcos A. Valdebenito, Matthias G. R. Faes, Michael Beer
Summary: Uncertainty quantification is crucial for reliability-oriented analysis and design of engineering structures. Three groups of mathematical models have been developed for different forms of uncertainties: probability models, imprecise probability models, and non-probabilistic models. Propagating these models through expensive simulators to quantify output uncertainties is a challenging task. Collaborative and Adaptive Bayesian Optimization (CABO) has been improved to handle all three categories of uncertainty models and to bound various probabilistic measures of the output.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
