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Data-driven probabilistic quantification and assessment of the prediction error model in damage detection applications

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PROBABILISTIC ENGINEERING MECHANICS 2023年 71卷

作者： Silionis, Nicholas E. Anyfantis, Konstantinos N. Natl Tech Univ Athens Sch Naval Architecture & Marine Engn Ship Hull Struct Hlth Monitoring S H SHM Grp 9 Heroon Polytech Ave Athens 15780 Greece

Advances in computer hardware and sensor technologies have led to a surge in the use of data-driven modeling and machine learning for structural engineering applications, with Structural Health Monitoring (SHM) being one of them. Despite considerable interest, it remains a research topic due to the difficulty in accurately quantifying aleatoric and epistemic uncertainty in SHM systems. Sources of uncertainty are related to operational and environmental variability, as well as measurement noise and the model prediction error associated with the data used to train damage identification algorithms. In this work, the authors aim to explicitly quantify the statistical structure of model prediction error and assess its influence on the detection performance of strain-based SHM architectures under the existence of aleatoric variability. A structural beam, subjected to probabilistic static loading is used as the reference structure and strain measurements as the damage-sensitive features. model prediction error is quantified explicitly using robust statistical tools through available laboratory observations and synthetic (Finite Element) data. Monte Carlo simulations enabled the forward propagation of uncertainty to the feature space to generate training data for three binary detectors (Likelihood Ratio Test, Quadratic Discriminant Analysis and Mahalanobis Distance), based on statistical pattern recognition. Detection performance was compared between the explicitly quantified prediction model error and the commonly assumed white Gaussian noise model, showcasing the influence of systematic error (bias) and correlation on the robustness of an SHM system using real-world data.

关键词： Uncertainty quantification model prediction error Strain sensors Detection theory

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A Survey of Maneuvering Target prediction via Interactive Multiple model

A Survey of Maneuvering Target Prediction via Interactive Mu...

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第34届中国控制与决策会议

作者： Wang Na Peng Kun Li Yang School of Control Science and Engineering Tiangong University Key Laboratory of Intelligent Control of Electrical Equipment

Maneuvering target prediction is a kind of methods to estimate the future state of moving target with randomness and complexity,according to the information of historical moment or current *** the target maneuvering,larger prediction error will be produced by single motion *** the interactive multiple model is *** this means,several models are used to match with their moving *** the parallel filtering is realized,and the results of filters are weighted and combined to obtain the final *** the prediction accuracy of the proposed method for the more complex motions is *** this paper,considering the common states of constant velocity,constant acceleration and variable acceleration,the principle of interactive multiple model is *** then its study and application in target prediction is ***,the development and the trends of the interactive multiple model in the target prediction is analyzed and prospected.

关键词： Maneuvering Target prediction Target State Estimation Motion model model prediction error Interactive Multiple model

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Bayesian identification of electromechanical properties in piezoelectric energy harvesters

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2020年 141卷 106506-000页

作者： Peralta, Patricio Ruiz, Rafael O. Taflanidis, Alexandros A. Univ Chile Dept Mech Engn Santiago Chile Univ Chile Dept Civil Engn Santiago Chile Univ Chile Ctr Modern Computat Engn Uncertainty Quantificat Grp Santiago Chile Univ Notre Dame Dept Civil & Environm Engn & Earth Sci Notre Dame IN 46556 USA Univ Notre Dame Dept Aerosp & Mech Engn Notre Dame IN 46556 USA

The model updating of the electro-mechanical properties of Piezoelectric Energy Harvesters (PEHs) using experimental data within a Bayesian inference setting is discussed. The implementation requires: a predictive model for the harvester response;an assumption for its prediction error;a prior multivariate probabilistic density function for the electromechanical properties;and experimental measurements of the harvester response. Different approaches are compared with respect to the Bayesian model updating, including point estimates of the updated properties based on Maximum a Posteriori and Maximum Likelihood Estimates, as well as a full description of the posterior density for the model characteristics, obtained through a Transitional Markov Chain Monte Carlo approach. A model class selection implementation is also discussed that allows for the consideration of some PEH properties as either deterministic or aleatoric (uncertain) variables. The overall framework offers an elegant approach to calibrate PEH numerical/analytical model or identify variability trends for the PEH manufacturing process. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Bayesian model updating Piezoelectric Energy Harvesters model class selection model prediction error Electro-mechanical properties identification

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ON ROBUST MULTI-PERIOD PRE-COMMITMENT AND TIME-CONSISTENT MEAN-VARIANCE PORTFOLIO OPTIMIZATION

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INTERNATIONAL JOURNAL OF THEORETICAL AND APPLIED FINANCE 2017年第7期20卷

作者： Cong, F. Oosterlee, C. W. Delft Univ Technol Delft Inst Appl Math Mekelweg 4 NL-2628 CD Delft Netherlands Ctr Wiskunde & Informat Sci Pk 123 NL-1098 XG Amsterdam Netherlands

We consider robust pre-commitment and time-consistent mean-variance optimal asset allocation strategies, that are required to perform well also in a worst-case scenario regarding the development of the asset price. We show that worst-case scenarios for both strategies can be found by solving a specific equation each time step. In the unconstrained asset allocation case, the robust pre-commitment as well as the time-consistent strategy are identical to the corresponding robust myopic strategies, by which investors perform robust portfolio control only for one time step and conduct a risk-free strategy afterwards. In the experiments, the robustness of pre-commitment and time-consistent strategies is studied in detail. Our analysis and numerical results indicate that the time-consistent allocation strategy is more stable when possible incorrect assumptions regarding the future asset development are modeled and taken into account. In some situations, the time-consistent strategy can even generate higher efficient frontiers than the pre-commitment strategy (which is counter-intuitive), because the time-consistency restriction appears to protect an investor in such a situation.

关键词： Robust optimization mean-variance optimal asset allocation target-based strategy time-consistent strategy model prediction error

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Probabilistic measures for assessing appropriateness of robust design optimization solutions

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2015年第4期51卷 813-834页

作者： Medina, Juan Camilo Taflanidis, Alexandros Univ Notre Dame Dept Aerosp & Mech Engn Notre Dame IN 46556 USA Univ Notre Dame Dept Civil & Environm Engn & Earth Sci Notre Dame IN 46556 USA

Robust design optimization (RDO) is a popular framework for addressing uncertainties in the design of engineering systems by considering different statistical measures, typically the mean and standard deviation of the system response. RDO can lead to a wide range of different candidate designs, establishing a different compromise between these competing objectives. This work introduces a new robustness measure, termed probability of dominance, for assessing the appropriateness of each candidate design. This measure is defined as the likelihood that a particular design will outperform the rival designs within a candidate set. Furthermore, a multi-stage implementation is introduced to facilitate increased versatility/confidence in the decision-making process by considering the comparison among smaller subsets within the initial larger set of candidate designs. For enhancing the robustness in these comparisons the impact of prediction errors, introduced to address potential differences between the real (i.e. as built) system and the numerical model adopted for it, is also addressed. This extends to proper modeling of the influence of the prediction error, including selection of its probability model, as well as evaluation of its impact on the probability of dominance and on the RDO formulation itself. Two illustrative examples are presented, the first considering the design of a tuned mass damper (TMD) for vibration mitigation of harmonic excitations and the second a topology optimization problem for minimum compliance. Extensive comparisons are presented in these two examples and the discussions demonstrate the power of the proposed approach for assessing the designs' robustness.

关键词： Robust design optimization Probability of dominance Uncertainty quantification model prediction error Probabilistic design

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H-infinity CONTROLLER DESIGN FOR UNCERTAIN NETWORKED CONTROL SYSTEMS WITH SCHEDULING STRATEGY BASED ON PREDICTED error

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2015年第4期11卷 1281-1293页

作者： Zhu, Qixin Lu, Kaihong Zhu, Yonghong Suzhou Univ Sci & Technol Sch Mech Engn 1701 Binhe Rd Suzhou 215009 Peoples R China East China Jiaotong Univ Sch Elect & Elect Engn Nanchang 330013 Jiangxi Peoples R China Jingdezhen Ceram Inst Sch Mech & Elect Engn Jingdezhen 333001 Peoples R China

For network environment, scheduling policy based on model prediction error and accomplished by the switch matrix is presented for networked control systems (NCS), in which the characters of NCS are considered, such as limited network bandwidth, limited node energy and high collision probability. Data is transmitted and computed only if the absolute value of prediction error is larger than the threshold value. And the model of NCS based on prediction error scheduling with uncertain parameters and network-induced delay is established. To make the model closer to real system, a constant time-delay model is also introduced in the NCS model. The H-infinity controller of such NCS is designed by using Lyapunov-Krasovskii function and inequality theory. Finally, simulations are included to demonstrate the theoretical results.

关键词： Networked control systems model prediction error Scheduling Uncertain Time delay H-infinity controller

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Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments

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JOURNAL OF HYDROLOGY 2013年 480卷 33-45页

作者： Ritter, Axel Munoz-Carpena, Rafael Univ La Laguna Dept Ingn Prod & Econ Agr San Cristobal la Laguna 38200 Spain Univ Florida Gainesville FL 32611 USA

Success in the use of computer models for simulating environmental variables and processes requires objective model calibration and verification procedures. Several methods for quantifying the goodness-of-fit of observations against model-calculated values have been proposed but none of them is free of limitations and are often ambiguous. When a single indicator is used it may lead to incorrect verification of the model. Instead, a combination of graphical results, absolute value error statistics (i.e. root mean square error), and normalized goodness-of-fit statistics (i.e. Nash-Sutcliffe Efficiency coefficient, NSE) is currently recommended. Interpretation of NSE values is often subjective, and may be biased by the magnitude and number of data points, data outliers and repeated data. The statistical significance of the performance statistics is an aspect generally ignored that helps in reducing subjectivity in the proper interpretation of the model performance. In this work, approximated probability distributions for two common indicators (NSE and root mean square error) are derived with bootstrapping (block bootstrapping when dealing with time series), followed by bias corrected and accelerated calculation of confidence intervals. Hypothesis testing of the indicators exceeding threshold values is proposed in a unified framework for statistically accepting or rejecting the model performance. It is illustrated how model performance is not linearly related with NSE, which is critical for its proper interpretation. Additionally, the sensitivity of the indicators to model bias, outliers and repeated data is evaluated. The potential of the difference between root mean square error and mean absolute error for detecting outliers is explored, showing that this may be considered a necessary but not a sufficient condition of outlier presence. The usefulness of the approach for the evaluation of model performance is illustrated with case studies including those with similar g

关键词： Block bootstrapping Coefficient of efficiency Hypothesis testing model prediction error

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model Selection Among Physics-Based models

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JOURNAL OF MECHANICAL DESIGN 2013年第2期135卷 021003-021003页

作者： Hombal, V. K. Mahadevan, S. Vanderbilt Univ Dept Civil & Environm Engn Nashville TN 37235 USA

The optimal solution of a design optimization problem is dependent on the predictive models used to evaluate the objective and constraints. Since different models give different predictions and can yield different design decisions, when more than one model is available, the choice of model used to represent the objectives/constraints of the design becomes important. This paper addresses the problem of model selection among physics-based models during the prediction stage, which is in contrast to model selection during the calibration and validation stages, and therefore affects design under uncertainty. model selection during calibration addresses the problem of selecting the model that is likely to provide the best generalization of the calibration data over the entire domain. model selection during the validation stage examines the validity of a calibrated model by comparing its predictions against the validation data. This paper presents an approach that allows for model selection during the prediction stage, which selects the "best" model for each prediction point. The proposed approach is based on the estimation of the model prediction error under stationary/nonstationary uncertainty. By selecting the best model at each prediction point, the proposed approach partitions the input domain of the models into nonoverlapping regions. The effects of measurement noise, sparseness of validation data, and model prediction uncertainty are included in deriving a probabilistic selection criterion for model selection. The effects of these uncertainties on the classification errors are analyzed. The proposed approach is demonstrated for the problem of selecting between two parametric models for energy dissipation in a mechanical lap joint under dynamic loading, and for the problem of selecting fatigue crack growth models. [DOI: 10.1115/1.4023155]

关键词： model selection model prediction error Gaussian processes

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Reliability-Based Design Using Two-Stage Stochastic Optimization with a Treatment of model prediction errors

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JOURNAL OF ENGINEERING MECHANICS-ASCE 2010年第12期136卷 1460-1473页

作者： Taflanidis, Alexandros A. Beck, James L. Univ Notre Dame Dept Civil Engn & Geol Sci Notre Dame IN 46556 USA CALTECH Pasadena CA 91125 USA

Design problems that involve optimization of the reliability of engineering systems are the focus of this paper. Methodologies are discussed applicable to problems that involve nonlinear systems and a large number of uncertain parameters specifying the system and excitation models. To address the complexity of these problems, stochastic simulation is considered for evaluation of the system reliability. An innovative approach, called stochastic subset optimization, SSO is discussed for performing a sensitivity analysis with respect to the design variables of the problem as well as the uncertain model parameters. In a small number of iterations, SSO converges to a smaller subset of the original design space that has high plausibility of containing the optimal design variables and that consists of near-optimal designs. For higher accuracy, an appropriate stochastic optimization algorithm may then be used to pinpoint the optimal design variables within this subset. This produces an efficient two-stage framework for optimal reliability design. Topics related to the combination of the two different stages for overall enhanced efficiency are discussed. An example is presented that illustrates the effectiveness of the proposed two-stage methodology for a challenging dynamic reliability problem. Also, a study is performed of the influence on the optimal design decisions of the prediction error of the system model, which is introduced because no model makes perfect predictions of the system response.

关键词： Stochastic optimization Stochastic simulation model prediction error System reliability Robust design

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