ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Abstract In this paper, we provide a comprehensive definition and classification of various sources of uncertainty within the fields of Structural Dynamics, System Identification, and Structural Health Monitoring (SHM), with a primary focus on the latter. Utilizing the classical input-output system representation as a main contextual framework, we present a taxonomy of uncertainties, intended for consistent classification of uncertainties in SHM applications: (i) input uncertainty; (ii) model form uncertainty; (iii) model parameter/variable uncertainty; (iv) measurement uncertainty; and (v) inherent variability. We then critically review methods and algorithms that address these uncertainties in the context of key SHM tasks: system identification and model inference, model updating, accounting for environmental and operational variability, virtual sensing, damage identification, and prognostic health management. A benchmark shear frame model with hysteretic links is employed as a running example to illustrate the application of selected methods and algorithmic tools. Finally, we discuss open challenges and future research directions in uncertainty quantification for SHM.

Antonios Kamariotis

Konstantinos Vlachas

Vasileios Ntertimanis

Ioannis Koune

Alice Cicirello

Eleni Chatzi

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering

On the consistent classification and treatment of uncertainties in structural health monitoring applications

结构健康监测应用中不确定性的一致分类与处理

Abstract Additive manufacturing (AM) has been growing rapidly to transform industrial applications. However, the fundamental mechanism of AM has not been fully understood which resulted in low success rate of building. A remedy is to introduce surrogate modeling based on experimental dataset to assist additive design and increase design efficiency. As one of the first papers for predictive modeling of AM especially direct energy deposition (DED), this paper discusses a bidirectional modeling framework and its application to multiple DED benchmark designs including: (1) forward prediction with cross-validation, (2) global sensitivity analyses, (3) backward prediction and optimization, and (4) intelligent data addition. Approximately 1150 mechanical tensile test samples were extracted and tested with input variables from machine parameters, postprocess, and output variables from mechanical, microstructure, and physical properties.

Yiming Zhang

Sreekar Karnati

Soumya Nag

Neil Johnson

Genghis Khan

Brandon Ribic

Accelerating Additive Design With Probabilistic Machine Learning

加速增材设计与概率机器学习

Abstract Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and Functional Principal Component Analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm-assisted orthogonal matching pursuit for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.

Declan Mallamo

Michael Azarian

Michael Pecht

Daily Engine Performance Trending using Common Flight Regime Identification

使用常见飞行状态识别的发动机性能日常趋势分析

Abstract Direct inverse analysis of faults in machinery systems such as gears using first principle is intrinsically difficult, owing to the multiple time- and length-scales involved in vibration modeling. As such, data-driven approaches have been the mainstream whereas supervised trainings are deemed effective. Nevertheless, existing techniques often fall short in their ability to generalize from discrete data labels to the continuous spectrum of possible faults which is further compounded by various uncertainties. This research proposes an interpretability-enhanced deep learning framework that incorporates Bayesian principles, effectively transforming convolutional neural networks into dynamic predictive models and significantly amplifying their generalizability with more accessible insights of the model's reasoning processes. Our approach is distinguished by a novel implementation of Bayesian inference, enabling the navigation of the probabilistic nuances of gear fault severities. By integrating variational inference into the deep learning architecture, we present a methodology that excels in leveraging limited data labels to reveal insights into both observed and unobserved fault conditions. This approach improves the model's capacity for uncertainty estimation and probabilistic generalization. Experimental validation on a lab-scale gear setup demonstrated the framework's superior performance, achieving nearly 100% accuracy in classifying known fault conditions, even in the presence of significant noise, and maintaining 96.15% accuracy when dealing with unseen fault severities. These results underscore the method's capability in discovering implicit relations between known and unseen faults, facilitating extended fault diagnosis, and effectively managing large degrees of measurement uncertainties.

Zhou Kai

Qianyu Zhou

Jiong Tang

Harnessing Bayesian Deep Learning to Tackle Unseen and Uncertain Scenarios in Diagnosis of Machinery Systems

利用贝叶斯深度学习应对机械系统诊断中的未知和不确定情景

Abstract This paper investigates the impact of threshold-crossing events on ship capsizing through a probabilistic model that predicts random wave heights. Utilizing statistical data from wave height observations, this paper proposes that random waves can be approximated and modeled using the Wiener process, employing autocorrelation function identification and probabilistic statistical verification methods. The threshold-crossing duration of a random wave is just the period of the wave exceeds a given threshold, which can reflect the frequency property of the stochastic sea waves. And the probability distribution of the period can theoretically be determined using the derived probability density function for the time interval between any two adjacent crossings of the Wiener process and a threshold. Based on the above theories, the capsizing probabilities of a civil ship sailing in different wave areas are analyzed under different safety thresholds considering certain ratios of the ship's intrinsic period and wave period. The research results can provide a reference for the anti-overturning design of the ships under the action of random waves.

Zhenhao Zhang

Guoqing Wei

Zhe Zeng

Fengwei Teng

The Safety Assessment of Civil Ships against Capsizing Based on the Probability Analysis of Multiple Threshold-crossings of Stochastic Sea Waves

基于随机海浪多阈值穿越概率的民用船舶倾覆安全性评估

Abstract The organic coating-substrate structure suffers from corrosion reaction between the substrate material and water molecules during the storage stage. Digital twin is a promising tool for corrosion modeling and assessing the reliability of the organic coating-substrate structure. In this paper, a multi-state modelling method is proposed towards the digital twin and reliability modeling and analysis. Firstly, to analyze the physicochemical process involved in the corrosion of organic coating-substrate structures, a multi-physics simulation method is developed. Then, the degradation performance of the organic coating-substrate structure is discretized into several states, and a Markov model is utilized to model the degradation process of the structure to support the digital twine model construction. The transition intensities of the Markov model are estimated by using the multi-physics simulation data. In the proposed method, the multi-physics simulation method can incorporate the diffusion equation and the kinetic equation of the corrosion, allowing for the simulation of water molecule diffusion within the organic coating and the coupling simulation of the metal corrosion process. Subsequently, the reliability of the organic coating-substrate structure is analyzed under varying temperatures, humidity levels, and protective material parameters. The result shows that higher ambient temperatures and relative humidity levels contribute to an accelerated corrosion rate of the substrate, and the reliability decreases.

Chaoyang Xie

Hao Zhang

Multi-State Reliability Modeling and Assessment for Corrosion of Organic Coating-Substrate Structure

有机涂层-基底结构腐蚀的多状态可靠性建模与评估

Abstract With advances in additive manufacturing, the technology has significantly increased the applications in a wide range of industrial sectors. For example, stereolithography has become a promising candidate for the mass production of energy absorption architected cellular materials due to its capability to fabricate complex material designs with advantageous characteristics. As stereolithography is being applied in different industrial settings, uncertainties become a critical factor that influences the performance of the products. As a solution, uncertainty quantification is needed to understand the impact of uncertainties on the overall performance variability of the design and inform decision-makers to enhance system robustness and reliability better. This paper presented a novel framework for accelerated uncertainty quantification based on the integration of physics-based computational modeling and data-driven surrogate models. Based on experimental tests, the high-fidelity finite element model can be built and validated. With an adaptive sampling technique, the surrogate model can be built with fewer expensive simulation runs while achieving a desirable modeling accuracy, which in turn saves the computational cost. Then, uncertainty quantification can be conducted accordingly using the surrogate model being developed, which provided insights for design and manufacturing decision-making processes of the architected cellular materials utilizing the additive manufacturing technology.

Zheng Liu

Yanwen Xu

Yuan Jiang

Anabel Renteria

Parth Bansal

Chenlong Xu

Pingfeng Wang

Yumeng Li

Uncertainty Quantification of Additively Manufactured Architected Cellular Materials for Energy Absorption Applications

增材制造结构多孔材料能量吸收应用的不确定性量化

短名
Journal Impact	1.98
国际分区	ENGINEERING, MULTIDISCIPLINARY(Q2)

ISSN	2332-9017, 2332-9025
h-index	19

涉及主题	计算机科学工程类物理数学量子力学统计热力学人工智能经济生物哲学材料科学业务操作系统程序设计语言医学机器学习化学可靠性工程地质学机械工程复合材料功率（物理）地理管理
出版信息	出版商: The American Society of Mechanical Engineers(ASME)，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2015，原创研究文献占比: 100.00%，自引率:5.60%， Gold OA占比: 1.64%

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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On the consistent classification and treatment of uncertainties in structural health monitoring applications

Accelerating Additive Design With Probabilistic Machine Learning

Daily Engine Performance Trending using Common Flight Regime Identification

Harnessing Bayesian Deep Learning to Tackle Unseen and Uncertain Scenarios in Diagnosis of Machinery Systems

The Safety Assessment of Civil Ships against Capsizing Based on the Probability Analysis of Multiple Threshold-crossings of Stochastic Sea Waves

On the consistent classification and treatment of uncertainties in structural health monitoring applications

Daily Engine Performance Trending using Common Flight Regime Identification

Harnessing Bayesian Deep Learning to Tackle Unseen and Uncertain Scenarios in Diagnosis of Machinery Systems

Multi-State Reliability Modeling and Assessment for Corrosion of Organic Coating-Substrate Structure

Uncertainty Quantification of Additively Manufactured Architected Cellular Materials for Energy Absorption Applications